Evaluation metrics and statistical tests for machine learning
Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the...
Saved in:
| Published in: | Scientific reports Vol. 14; no. 1; pp. 6086 - 14 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
13.03.2024
Nature Publishing Group Nature Portfolio |
| Subjects: | |
| ISSN: | 2045-2322, 2045-2322 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images. |
|---|---|
| AbstractList | Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images. Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images.Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images. Abstract Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images. |
| ArticleNumber | 6086 |
| Author | Teuho, Jarmo Klén, Riku Rainio, Oona |
| Author_xml | – sequence: 1 givenname: Oona orcidid: 0000-0002-7775-7656 surname: Rainio fullname: Rainio, Oona email: ormrai@utu.fi organization: Turku PET Centre, University of Turku and Turku University Hospital – sequence: 2 givenname: Jarmo orcidid: 0000-0001-9401-0725 surname: Teuho fullname: Teuho, Jarmo organization: Turku PET Centre, University of Turku and Turku University Hospital – sequence: 3 givenname: Riku orcidid: 0000-0002-0982-8360 surname: Klén fullname: Klén, Riku organization: Turku PET Centre, University of Turku and Turku University Hospital |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38480847$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9UktPFTEYbQxGEPkDLswkbtwM9v1YGEMICgmJG103nT4uvZlpsZ0h-O8tdxCBBd20-XrO6en3nbdgL-XkAXiP4DGCRH6uFDEle4hpz7iAvL99BQ4wpKzHBOO9R-d9cFTrFrbFsKJIvQH7RFIJJRUH4MvZjRkXM8ecusnPJdrameS6OrdanaM1Yzf7Otcu5NJNxl7F5LvRm5Ji2rwDr4MZqz-63w_Br29nP0_P-8sf3y9OTy57yyiaeyKEs9wwwrhykLvBBz4EqQLlWAlFmXNhIJ4xg7zBxAXMMLfUOucUHBQmh-Bi1XXZbPV1iZMpf3Q2Ue8KuWy0Kc3s6LUUgVJEFQpuoGTAUiHFvJGDCJIb55rW11Xrehkm76xPczHjE9GnNyle6U2-0QgqIjhVTeHTvULJv5fWHD3Fav04muTzUjVWTCDOBaIN-vEZdJuXklqv7lCcIUIkbKgPjy09ePk3pgbAK8CWXGvx4QGCoL6Lg17joFsc9C4O-raR5DOSjfNu0u1bcXyZSlZqbe-kjS__bb_A-gvdwMot |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2025_3584376 crossref_primary_10_1021_acsomega_5c00549 crossref_primary_10_3390_jimaging11020059 crossref_primary_10_1016_j_eswa_2025_128227 crossref_primary_10_1007_s11120_025_01140_x crossref_primary_10_1016_j_bspc_2025_108470 crossref_primary_10_55056_jec_978 crossref_primary_10_1145_3742435 crossref_primary_10_1371_journal_pone_0328880 crossref_primary_10_1108_SEF_09_2024_0629 crossref_primary_10_3390_rs17172928 crossref_primary_10_1109_ACCESS_2025_3574451 crossref_primary_10_3390_w16202986 crossref_primary_10_1057_s41599_025_05490_8 crossref_primary_10_1016_j_jneumeth_2025_110555 crossref_primary_10_1016_j_tre_2024_103943 crossref_primary_10_7717_peerj_cs_2800 crossref_primary_10_1016_j_enbuild_2025_116465 crossref_primary_10_1109_ACCESS_2025_3555810 crossref_primary_10_1016_j_eswa_2025_128465 crossref_primary_10_1007_s11227_025_07522_1 crossref_primary_10_1016_j_econmod_2025_107225 crossref_primary_10_1109_ACCESS_2025_3600146 crossref_primary_10_1007_s13349_025_00908_y crossref_primary_10_1016_j_compbiomed_2025_110347 crossref_primary_10_2196_66735 crossref_primary_10_1186_s13040_024_00410_z crossref_primary_10_3390_s25103208 crossref_primary_10_1177_14759217251376610 crossref_primary_10_3390_s25082412 crossref_primary_10_1007_s11269_024_04069_3 crossref_primary_10_1016_j_cropro_2024_107035 crossref_primary_10_1007_s43621_025_01541_x crossref_primary_10_1088_1361_6528_adf4ef crossref_primary_10_1016_j_bir_2025_06_013 crossref_primary_10_1038_s41598_025_01860_z crossref_primary_10_3390_systems12100415 crossref_primary_10_1007_s41060_025_00763_6 crossref_primary_10_1177_10732748251332803 crossref_primary_10_3390_w16223328 crossref_primary_10_1093_jamia_ocae290 crossref_primary_10_1038_s41598_025_08824_3 crossref_primary_10_1016_j_engappai_2024_109475 crossref_primary_10_1136_rmdopen_2024_004309 crossref_primary_10_1002_adma_202403411 crossref_primary_10_3390_app14083297 crossref_primary_10_1109_ACCESS_2025_3558878 crossref_primary_10_1016_j_jclepro_2024_143828 crossref_primary_10_3389_fdgth_2025_1484231 crossref_primary_10_1177_0272989X251343082 crossref_primary_10_1002_ima_23180 crossref_primary_10_3390_wevj16010036 crossref_primary_10_1016_j_neurol_2025_02_007 crossref_primary_10_1016_j_heliyon_2024_e38101 crossref_primary_10_1093_ofid_ofaf392 crossref_primary_10_1016_j_buildenv_2025_113286 crossref_primary_10_1016_j_mtchem_2025_102640 crossref_primary_10_3390_biomimetics10080488 crossref_primary_10_1016_j_ab_2024_115546 crossref_primary_10_2196_52794 crossref_primary_10_2196_58899 crossref_primary_10_1016_j_jpurol_2024_10_003 crossref_primary_10_1007_s12055_025_01941_8 crossref_primary_10_1016_j_imu_2025_101682 crossref_primary_10_1039_D5AN00117J crossref_primary_10_1016_j_mtcomm_2024_111000 crossref_primary_10_1140_epjs_s11734_025_01720_x crossref_primary_10_1158_1940_6207_CAPR_24_0253 crossref_primary_10_1016_j_ijpharm_2025_126046 crossref_primary_10_3390_diagnostics15182308 crossref_primary_10_1155_acis_9955073 crossref_primary_10_3390_su17167464 crossref_primary_10_1016_j_asoc_2024_112307 crossref_primary_10_3389_fgene_2025_1589999 crossref_primary_10_1007_s12539_025_00723_5 crossref_primary_10_1016_j_engappai_2025_111627 crossref_primary_10_1109_ACCESS_2025_3549271 crossref_primary_10_7717_peerj_cs_2333 crossref_primary_10_1109_ACCESS_2024_3434670 crossref_primary_10_1177_09544089251327716 crossref_primary_10_1016_j_jestch_2024_101883 crossref_primary_10_3390_agriengineering7010013 crossref_primary_10_1016_j_heliyon_2024_e39205 crossref_primary_10_2196_63701 crossref_primary_10_3390_metabo14090483 crossref_primary_10_1007_s00521_024_09897_3 crossref_primary_10_3390_molecules30143043 crossref_primary_10_1016_j_ecoenv_2025_118610 crossref_primary_10_1080_22797254_2025_2517381 crossref_primary_10_1007_s13246_025_01626_x crossref_primary_10_1016_j_isci_2025_112593 crossref_primary_10_3389_fphys_2025_1659098 crossref_primary_10_1007_s13721_025_00556_8 crossref_primary_10_1007_s10439_024_03611_z crossref_primary_10_3389_fdata_2025_1521653 crossref_primary_10_1016_j_ejvs_2024_07_003 crossref_primary_10_3390_rs17071189 crossref_primary_10_59400_sv3523 crossref_primary_10_1016_j_jsames_2025_105756 crossref_primary_10_1007_s42044_025_00288_y crossref_primary_10_3390_fishes10070348 crossref_primary_10_1016_j_marenvres_2025_107170 crossref_primary_10_1016_j_scs_2025_106795 crossref_primary_10_1109_JSEN_2025_3587247 crossref_primary_10_1080_03091902_2025_2466834 crossref_primary_10_1016_j_intermet_2025_108921 crossref_primary_10_3390_en18102465 crossref_primary_10_1016_j_cscm_2024_e04106 crossref_primary_10_1007_s13369_025_10215_9 crossref_primary_10_1007_s10994_024_06701_0 crossref_primary_10_3389_fpsyt_2025_1648585 crossref_primary_10_1016_j_watres_2025_123884 crossref_primary_10_3390_s24165231 crossref_primary_10_1186_s12888_025_06536_6 crossref_primary_10_1007_s40171_025_00464_w crossref_primary_10_1111_epi_18521 crossref_primary_10_1016_j_watres_2025_124616 crossref_primary_10_48084_etasr_10527 crossref_primary_10_1016_j_foodchem_2025_143346 crossref_primary_10_1016_j_semcancer_2025_02_009 crossref_primary_10_1038_s41598_024_71480_6 crossref_primary_10_1016_j_asoc_2024_112646 crossref_primary_10_1007_s11282_025_00839_w crossref_primary_10_3390_ma18061386 crossref_primary_10_1016_j_compbiolchem_2025_108648 crossref_primary_10_1016_j_eswa_2025_127984 crossref_primary_10_1016_j_foreco_2025_123067 crossref_primary_10_3390_life15040594 crossref_primary_10_1016_j_ymeth_2025_03_023 crossref_primary_10_1371_journal_pone_0327137 crossref_primary_10_1007_s00603_024_04287_6 crossref_primary_10_1038_s41598_025_98366_5 crossref_primary_10_3390_app14219863 crossref_primary_10_3389_fmed_2025_1547588 crossref_primary_10_3390_a18060310 crossref_primary_10_3389_fmed_2025_1631565 crossref_primary_10_1016_j_cmpb_2025_109036 crossref_primary_10_1371_journal_pone_0319998 crossref_primary_10_1016_j_iot_2024_101284 crossref_primary_10_1007_s11600_025_01624_3 crossref_primary_10_1080_17477778_2025_2549092 crossref_primary_10_1140_epjds_s13688_025_00569_3 crossref_primary_10_1093_cjres_rsaf021 crossref_primary_10_1016_j_commatsci_2025_114200 crossref_primary_10_1016_j_jafrearsci_2024_105487 crossref_primary_10_3390_app142311342 crossref_primary_10_1016_j_engappai_2025_111815 crossref_primary_10_1093_nar_gkaf444 crossref_primary_10_1016_j_fuel_2025_136356 crossref_primary_10_1109_ACCESS_2025_3552529 crossref_primary_10_1051_itmconf_20246904008 crossref_primary_10_1093_bib_bbaf385 crossref_primary_10_3389_frai_2024_1325219 crossref_primary_10_3390_ijms26178407 crossref_primary_10_3390_polym17050700 crossref_primary_10_1177_20552076251350755 crossref_primary_10_1371_journal_pone_0312208 crossref_primary_10_1097_EDE_0000000000001803 crossref_primary_10_1186_s12879_025_10738_4 crossref_primary_10_3390_diagnostics15010105 crossref_primary_10_1038_s41598_025_02939_3 crossref_primary_10_1117_1_JMI_12_2_024007 crossref_primary_10_1080_08839514_2025_2515063 crossref_primary_10_1145_3699744 crossref_primary_10_15672_hujms_1608393 crossref_primary_10_3390_pharmaceutics17030308 crossref_primary_10_1021_acsomega_5c01473 crossref_primary_10_1145_3715159 crossref_primary_10_1016_j_jisa_2025_104096 crossref_primary_10_1021_acs_jcim_5c01609 crossref_primary_10_1109_ACCESS_2025_3607720 crossref_primary_10_1038_s41598_025_08004_3 crossref_primary_10_1186_s12859_025_06152_x crossref_primary_10_3390_buildings14061878 crossref_primary_10_1002_spy2_403 crossref_primary_10_3390_s24175804 crossref_primary_10_1177_02537176241311196 crossref_primary_10_3389_fmars_2025_1549513 crossref_primary_10_3897_oneeco_10_e149055 crossref_primary_10_1109_ACCESS_2025_3593364 crossref_primary_10_3390_bioengineering12020144 crossref_primary_10_3390_app142210532 crossref_primary_10_3390_fi17040164 crossref_primary_10_1016_j_agwat_2025_109615 crossref_primary_10_1016_j_eij_2025_100775 crossref_primary_10_3389_fpls_2025_1513953 crossref_primary_10_1016_j_ijepes_2025_110526 crossref_primary_10_1016_j_seps_2025_102213 crossref_primary_10_1016_j_fuel_2024_134153 crossref_primary_10_3390_ijms252312942 crossref_primary_10_1016_j_forsciint_2025_112548 crossref_primary_10_1080_10589759_2025_2457593 crossref_primary_10_1080_1206212X_2025_2531033 crossref_primary_10_1109_ACCESS_2025_3540841 crossref_primary_10_1038_s41598_025_93505_4 crossref_primary_10_1016_j_jwpe_2025_108506 crossref_primary_10_1109_ACCESS_2025_3568831 crossref_primary_10_1186_s13321_025_01071_8 crossref_primary_10_3390_su16114411 crossref_primary_10_1016_j_compbiomed_2025_109988 crossref_primary_10_1088_1361_6560_adc96c crossref_primary_10_3390_sci6040084 crossref_primary_10_1016_j_bspc_2024_107159 crossref_primary_10_1371_journal_pone_0327960 crossref_primary_10_1007_s00468_024_02573_y crossref_primary_10_3390_buildings14123774 crossref_primary_10_3390_f16060938 crossref_primary_10_1038_s41598_025_04072_7 crossref_primary_10_3390_cancers17101604 crossref_primary_10_1016_j_mex_2025_103462 crossref_primary_10_1109_ACCESS_2025_3530927 crossref_primary_10_3390_fi17040176 crossref_primary_10_1007_s00330_025_11890_w crossref_primary_10_1109_ACCESS_2024_3451012 crossref_primary_10_1007_s44352_025_00011_4 crossref_primary_10_3390_s25082496 crossref_primary_10_1007_s12672_025_02734_6 crossref_primary_10_1007_s10791_025_09549_7 crossref_primary_10_1016_j_eswa_2025_128871 crossref_primary_10_1007_s42452_025_06514_3 crossref_primary_10_1016_j_measurement_2025_118927 crossref_primary_10_1007_s10489_025_06461_x crossref_primary_10_1016_j_isci_2025_111881 crossref_primary_10_1080_17452759_2024_2449171 crossref_primary_10_3389_frai_2025_1446876 crossref_primary_10_1007_s10044_025_01513_x crossref_primary_10_1007_s10278_025_01535_1 crossref_primary_10_1007_s41060_024_00584_z crossref_primary_10_1038_s41598_025_11116_5 crossref_primary_10_1109_ACCESS_2025_3526988 crossref_primary_10_1016_j_isatra_2024_12_008 crossref_primary_10_3390_s25020472 crossref_primary_10_1007_s11571_025_10253_x crossref_primary_10_3389_fcimb_2025_1545646 crossref_primary_10_3390_min14111160 crossref_primary_10_1109_ACCESS_2025_3565901 crossref_primary_10_1109_ACCESS_2025_3584401 crossref_primary_10_3390_app15020861 crossref_primary_10_1108_IJPHM_08_2024_0089 crossref_primary_10_1016_j_compbiomed_2025_110392 crossref_primary_10_3390_electronics13183700 crossref_primary_10_1002_aisy_202500455 crossref_primary_10_1038_s41598_025_98473_3 crossref_primary_10_1007_s00521_025_11397_x crossref_primary_10_1016_j_sab_2024_107111 crossref_primary_10_1038_s41598_025_09957_1 crossref_primary_10_1038_s41598_024_70545_w crossref_primary_10_1186_s40537_025_01238_y crossref_primary_10_1515_cppm_2025_0074 crossref_primary_10_3389_fneur_2025_1650350 crossref_primary_10_1016_j_ecoinf_2025_103260 crossref_primary_10_1016_j_compbiomed_2025_110267 crossref_primary_10_1109_JSEN_2024_3488278 crossref_primary_10_3390_land13122173 crossref_primary_10_1021_acs_jcim_4c02205 crossref_primary_10_3390_computers13100244 crossref_primary_10_1007_s10064_025_04161_x crossref_primary_10_4271_10_08_04_0027 crossref_primary_10_1186_s13636_025_00416_3 crossref_primary_10_1016_j_epsr_2025_111973 crossref_primary_10_3390_su17030972 crossref_primary_10_1177_00368504251350763 crossref_primary_10_1038_s41598_025_12117_0 crossref_primary_10_1016_j_ejrad_2024_111712 crossref_primary_10_1016_j_ijbiomac_2025_139519 crossref_primary_10_1109_ACCESS_2024_3506773 crossref_primary_10_3390_app15010111 crossref_primary_10_1109_ACCESS_2024_3521497 crossref_primary_10_3390_electronics14010209 crossref_primary_10_1016_j_eswa_2025_126482 crossref_primary_10_3390_ph18060776 crossref_primary_10_1088_1741_2552_adf888 crossref_primary_10_1093_jamia_ocaf059 crossref_primary_10_1016_j_eswa_2025_127697 crossref_primary_10_1088_1752_7163_ad9b46 crossref_primary_10_1080_13683500_2025_2456080 crossref_primary_10_1016_j_atech_2025_100806 crossref_primary_10_1016_j_measurement_2025_118917 crossref_primary_10_1007_s11273_025_10066_z crossref_primary_10_22399_ijcesen_1966 crossref_primary_10_1016_j_csbj_2024_10_019 crossref_primary_10_3390_neurosci6030073 crossref_primary_10_3389_fdata_2025_1522578 crossref_primary_10_1177_18724981241300907 crossref_primary_10_3390_app142210343 crossref_primary_10_3390_sym16101346 crossref_primary_10_1186_s41747_025_00599_6 crossref_primary_10_3390_jmse13091627 crossref_primary_10_3390_su17031261 crossref_primary_10_7717_peerj_cs_2436 crossref_primary_10_1038_s41746_025_01449_w crossref_primary_10_1088_1361_6560_adf2f4 crossref_primary_10_1093_labmed_lmaf013 crossref_primary_10_1038_s41598_025_96100_9 crossref_primary_10_3390_technologies13010034 crossref_primary_10_1016_j_ejmp_2025_105066 crossref_primary_10_1148_radiol_241629 crossref_primary_10_1016_j_modpat_2024_100663 crossref_primary_10_1016_j_ejrai_2025_100030 crossref_primary_10_1007_s10343_024_01100_w crossref_primary_10_1016_j_neunet_2025_107897 crossref_primary_10_3390_rs16132450 crossref_primary_10_1016_j_bios_2025_117394 crossref_primary_10_1016_j_compbiomed_2025_111107 crossref_primary_10_3390_data10030027 crossref_primary_10_1038_s41598_024_84360_w crossref_primary_10_1016_j_ebiom_2024_105401 crossref_primary_10_1016_j_bspc_2025_108571 crossref_primary_10_1016_j_asoc_2025_113291 crossref_primary_10_3390_foods13243986 crossref_primary_10_1088_1757_899X_1327_1_012030 crossref_primary_10_1109_ACCESS_2025_3551152 crossref_primary_10_1186_s12984_025_01570_7 crossref_primary_10_3390_app15189894 crossref_primary_10_1016_j_addma_2025_104929 crossref_primary_10_3390_app15020933 crossref_primary_10_1016_j_asoc_2025_113298 crossref_primary_10_2196_72113 crossref_primary_10_1016_j_rineng_2025_105688 crossref_primary_10_3389_fvets_2024_1459553 crossref_primary_10_1016_j_engappai_2025_112186 crossref_primary_10_1007_s12016_025_09076_9 crossref_primary_10_1016_j_ins_2024_120786 crossref_primary_10_1021_acs_jcim_5c00927 crossref_primary_10_1080_1064119X_2025_2550000 crossref_primary_10_1038_s41598_024_70773_0 crossref_primary_10_1016_j_jbi_2025_104825 crossref_primary_10_1371_journal_pone_0305654 crossref_primary_10_1007_s42600_025_00417_3 crossref_primary_10_1016_j_sciaf_2024_e02398 crossref_primary_10_1007_s00330_024_11321_2 crossref_primary_10_1302_0301_620X_106B7_BJJ_2024_0136 crossref_primary_10_1142_S0218126625300065 crossref_primary_10_1016_j_aap_2024_107890 crossref_primary_10_1016_j_jclimf_2024_100058 crossref_primary_10_1002_cend_202400049 crossref_primary_10_1038_s41598_025_88251_6 crossref_primary_10_1080_13658816_2025_2512218 crossref_primary_10_1057_s41272_025_00546_5 crossref_primary_10_3390_foods14101808 crossref_primary_10_1007_s40860_025_00248_0 crossref_primary_10_1016_j_dcmed_2025_05_007 crossref_primary_10_1002_smsc_202500173 crossref_primary_10_1016_j_knosys_2025_114153 crossref_primary_10_1109_TDSC_2024_3520155 crossref_primary_10_1186_s12911_025_02865_4 |
| Cites_doi | 10.1007/s42600-023-00314-7 10.1007/s40846-023-00818-8 10.1016/j.eswa.2021.114820 10.2307/2531595 10.1093/biomet/52.3-4.591 10.1109/ACCESS.2020.3010287 10.1109/TIP.2011.2173206 10.1109/TNNLS.2021.3084827 10.7717/peerj.8052 10.1007/978-981-16-6265-2_3 10.1177/001316446002000104 10.1038/s41592-019-0686-2 10.1126/science.aaa8415 10.1002/cpe.3745 10.1145/1148170.1148262 10.1080/03610928008827904 10.1007/s10462-015-9433-y 10.1186/1471-2105-12-77 10.1016/j.jda.2012.10.002 10.1016/j.cell.2018.02.010 10.1109/ACCESS.2020.3031384 10.1007/978-3-319-24574-4_28 10.1109/IEMBS.2003.1279826 10.1007/978-3-031-13339-8 10.1016/j.asoc.2023.110020 10.1109/TKDE.2013.39 10.1201/9780429170140 10.1007/978-3-030-12939-2_43 10.1023/A:1009752403260 10.1002/9781118165881 10.1371/journal.pone.0177678 10.1007/s10278-023-00812-1 10.2307/1932409 10.1038/s41598-023-37603-1 10.1111/j.1469-8137.1912.tb05611.x 10.1007/978-3-319-05885-6_17 10.1080/00365510701666031 10.1177/0962280207087173 10.1016/j.compbiomed.2021.104319 10.1016/j.compbiomed.2021.104324 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3 10.1177/0962280214541852 10.1002/9781118346990 10.1177/1536867X0900900101 10.25080/Majora-92bf1922-011 10.1016/j.ifacol.2020.12.1888 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024. corrected publication 2024 2024. The Author(s). The Author(s) 2024. corrected publication 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2024 |
| Copyright_xml | – notice: The Author(s) 2024. corrected publication 2024 – notice: 2024. The Author(s). – notice: The Author(s) 2024. corrected publication 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2024 |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU COVID DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1038/s41598-024-56706-x |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability (subscription) ProQuest Central UK/Ireland ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College Coronavirus Research Database ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection Medical Database Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2045-2322 |
| EndPage | 14 |
| ExternalDocumentID | oai_doaj_org_article_87f441491fdb43b289195ea8b7f86add PMC10937649 38480847 10_1038_s41598_024_56706_x |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Suomen Kulttuurirahasto funderid: http://dx.doi.org/10.13039/501100003125 – fundername: Jenny ja Antti Wihurin Rahasto funderid: http://dx.doi.org/10.13039/501100004022 |
| GroupedDBID | 0R~ 3V. 4.4 53G 5VS 7X7 88A 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M0L M1P M2P M48 M7P M~E NAO OK1 PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AASML AAYXX AFFHD AFPKN CITATION PHGZM PHGZT PJZUB PPXIY PQGLB CGR CUY CVF ECM EIF NPM 7XB 8FK COVID K9. PKEHL PQEST PQUKI PRINS Q9U 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c541t-377dc6a53569d06dbef6bf89f46297945ddfb3e55a1ea23df2526c4cddd90b923 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 420 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001185520800061&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2045-2322 |
| IngestDate | Fri Oct 03 12:42:00 EDT 2025 Tue Nov 04 02:06:21 EST 2025 Fri Sep 05 13:31:47 EDT 2025 Tue Oct 07 09:07:55 EDT 2025 Mon Jul 21 05:58:54 EDT 2025 Sat Nov 29 01:58:34 EST 2025 Tue Nov 18 22:24:37 EST 2025 Fri Feb 21 02:38:55 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Statistical testing Medical images Evaluation metrics Machine learning |
| Language | English |
| License | 2024. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c541t-377dc6a53569d06dbef6bf89f46297945ddfb3e55a1ea23df2526c4cddd90b923 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-7775-7656 0000-0001-9401-0725 0000-0002-0982-8360 |
| OpenAccessLink | https://www.proquest.com/docview/2956513380?pq-origsite=%requestingapplication% |
| PMID | 38480847 |
| PQID | 2956513380 |
| PQPubID | 2041939 |
| PageCount | 14 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_87f441491fdb43b289195ea8b7f86add pubmedcentral_primary_oai_pubmedcentral_nih_gov_10937649 proquest_miscellaneous_2957166714 proquest_journals_2956513380 pubmed_primary_38480847 crossref_primary_10_1038_s41598_024_56706_x crossref_citationtrail_10_1038_s41598_024_56706_x springer_journals_10_1038_s41598_024_56706_x |
| PublicationCentury | 2000 |
| PublicationDate | 2024-03-13 |
| PublicationDateYYYYMMDD | 2024-03-13 |
| PublicationDate_xml | – month: 03 year: 2024 text: 2024-03-13 day: 13 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2024 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | Levene, Olkin, Hotelling (CR56) 1960 Debats, Litjens, Huisman (CR6) 2019; 7 Robin, Turck, Hainard, Tiberti, Lisacek, Sanchez, Müller (CR52) 2011; 12 CR36 CR34 Kermany, Goldbaum, Cai (CR63) 2018; 172 Corder, Foreman (CR43) 2009 Bartlett (CR55) 1937; 160 Xiao, Ye, Esteves, Rong (CR29) 2016; 28 Tohka, Van Gils (CR12) 2021; 132 Qin, Hotilovac (CR50) 2008; 17 Lantz (CR21) 2023 Manning, Schutze (CR25) 1999 Jekel (CR40) 2007 Shapiro, Wilk (CR54) 1965; 52 Jaccard (CR33) 1912; 11 Kim, Lee (CR46) 2017; 26 Youden (CR18) 1950; 3 Bethea, Duran, Boullion (CR53) 1995 Pepe, Longton, Janes (CR23) 2009; 9 DeLong, DeLong, Clarke-Pearson (CR49) 1988; 44 CR5 CR8 CR7 Yilmaz, Demirhan (CR27) 2023; 134 CR48 Dehmer, Basak (CR14) 2012 Planche, Andres (CR10) 2019 Santafe, Inza, Lozano (CR11) 2015; 44 Virtanen, Gommers, Oliphant, Haberland, Reddy, Cournapeau, Burovski, Peterson, Weckesser, Bright, van der Walt, Brett, Wilson, Jarrod Millman, Mayorov, Nelson, Jones, Kern, Larson, Carey, Polat, Feng, Moore, VanderPlas, Axalde, Perktold, Cimrman, Henriksen, Quintero, Harris, Archibald, Ribeiro, Pedregosa, van Mulbregt (CR41) 2020; 17 Fox, Weisberg (CR57) 2019 Brunet, Vrscay, Wang (CR35) 2011; 21 (CR39) 2021 Sørensen (CR31) 1948; 5 Trajman, Luiz (CR47) 2008; 68 Sarkar, Sahoo (CR32) 2022 CR59 CR58 CR13 Šimundić (CR15) 2009; 19 Small Casler, Gawlik (CR16) 2022 Cohen (CR20) 1960; 20 Li, Liu, Yang, Peng, Zhou (CR9) 2021; 33 Rainio, Han, Teuho, Nesterov, Oikonen, Piirola, Laitinen, Tättäläinen, Knuuti, Klén (CR60) 2023; 36 Iman, Davenport (CR45) 1980; 9 Rahman, Khandakar, Qiblawey, Tahir, Kiranyaz, Kashem, Islam, Maadeed, Zughaier, Khan, Chowdhury (CR62) 2021; 132 Salzberg (CR44) 1997; 1 Demšar (CR4) 2006; 7 Lang, Secic (CR42) 2006 Rainio, Lahti, Anttinen, Ettala, Seppänen, Boström, Kemppainen, Klén (CR68) 2023 Jordan, Mitchell (CR1) 2015; 349 Zhang, Zhou (CR28) 2013; 26 Bertolini, Mezzogori, Neroni, Zammori (CR3) 2021; 175 Fradkov (CR2) 2020; 53 Hellström, Liedes, Rainio, Malaspina, Kemppainen, Klén (CR65) 2023; 13 Cox, Vladescu (CR17) 2023 Ronneberger, Fischer, Brox, Navab, Hornegger, Wells, Frangi (CR67) 2015 CR26 Emmert-Streib, Moutari, Dehmer (CR19) 2023 CR24 Dice (CR30) 1945; 26 Boughorbel, Jarray, El-Anbari (CR22) 2017; 12 Chowdhury, Rahman, Khandakar, Mazhar, Kadir, Mahbub, Islam, Khan, Iqbal, Al-Emadi, Reaz, Islam (CR61) 2020; 2020 van Rossum, Drake (CR38) 2009 Nakas, Bantis, Gatsonis (CR51) 2023 Rahman, Khandakar, Kadir, Islam, Islam, Mahbub, Ayari, Chowdhury (CR64) 2020; 8 Liedes, Hellström, Rainio, Murtojärvi, Malaspina, Hirvonen, Klén, Kemppainen (CR66) 2023 Dupret, Piwowarski (CR37) 2013; 18 MI Jordan (56706_CR1) 2015; 349 MEH Chowdhury (56706_CR61) 2020; 2020 AL Fradkov (56706_CR2) 2020; 53 ML Zhang (56706_CR28) 2013; 26 56706_CR8 DJ Cox (56706_CR17) 2023 C Manning (56706_CR25) 1999 56706_CR7 JF Jekel (56706_CR40) 2007 56706_CR5 B Lantz (56706_CR21) 2023 X Robin (56706_CR52) 2011; 12 DS Kermany (56706_CR63) 2018; 172 J Liedes (56706_CR66) 2023 J Cohen (56706_CR20) 1960; 20 P Jaccard (56706_CR33) 1912; 11 S Boughorbel (56706_CR22) 2017; 12 56706_CR36 56706_CR34 G van Rossum (56706_CR38) 2009 OA Debats (56706_CR6) 2019; 7 F Emmert-Streib (56706_CR19) 2023 AE Yilmaz (56706_CR27) 2023; 134 J Demšar (56706_CR4) 2006; 7 O Ronneberger (56706_CR67) 2015 56706_CR26 56706_CR24 WJ Youden (56706_CR18) 1950; 3 R Core Team (56706_CR39) 2021 (56706_CR16) 2022 Z Li (56706_CR9) 2021; 33 SS Shapiro (56706_CR54) 1965; 52 H Hellström (56706_CR65) 2023; 13 AM Šimundić (56706_CR15) 2009; 19 M Bertolini (56706_CR3) 2021; 175 T Sørensen (56706_CR31) 1948; 5 G Qin (56706_CR50) 2008; 17 M Dehmer (56706_CR14) 2012 M Pepe (56706_CR23) 2009; 9 T Rahman (56706_CR62) 2021; 132 LR Dice (56706_CR30) 1945; 26 C Xiao (56706_CR29) 2016; 28 J Fox (56706_CR57) 2019 S Kim (56706_CR46) 2017; 26 56706_CR59 T Rahman (56706_CR64) 2020; 8 56706_CR58 56706_CR13 D Brunet (56706_CR35) 2011; 21 O Rainio (56706_CR68) 2023 M Sarkar (56706_CR32) 2022 J Tohka (56706_CR12) 2021; 132 GW Corder (56706_CR43) 2009 O Rainio (56706_CR60) 2023; 36 G Dupret (56706_CR37) 2013; 18 G Santafe (56706_CR11) 2015; 44 SL Salzberg (56706_CR44) 1997; 1 H Levene (56706_CR56) 1960 RL Iman (56706_CR45) 1980; 9 A Trajman (56706_CR47) 2008; 68 CT Nakas (56706_CR51) 2023 B Planche (56706_CR10) 2019 P Virtanen (56706_CR41) 2020; 17 ER DeLong (56706_CR49) 1988; 44 RM Bethea (56706_CR53) 1995 TA Lang (56706_CR42) 2006 56706_CR48 MS Bartlett (56706_CR55) 1937; 160 38977765 - Sci Rep. 2024 Jul 8;14(1):15724. doi: 10.1038/s41598-024-66611-y |
| References_xml | – year: 1999 ident: CR25 publication-title: Foundations of Statistical Natural Language Processing – year: 2023 ident: CR21 publication-title: Machine Learning with R: Learn Techniques for Building and Improving Machine Learning Models, from Data Preparation to Model Tuning, Evaluation, and Working with Big Data – start-page: 234 year: 2015 end-page: 241 ident: CR67 article-title: U-Net: Convolutional networks for biomedical image segmentation publication-title: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. MICCAI 2015 – year: 2009 ident: CR43 publication-title: Nonparametric Statistics for Non-statisticians – volume: 17 start-page: 207 issue: 2 year: 2008 end-page: 221 ident: CR50 article-title: Comparison of non-parametric confidence intervals for the area under the ROC curve of a continuous-scale diagnostic test publication-title: Stat. Methods Med. Res. – start-page: 309 year: 2022 end-page: 335 ident: CR32 article-title: Intelligent image segmentation methods using deep convolutional neural network publication-title: Biomedical Signal and Image Processing with Artificial Intelligence – volume: 33 start-page: 6999 issue: 12 year: 2021 ident: CR9 article-title: A survey of convolutional neural networks: Analysis, applications, and prospects publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 26 start-page: 1819 issue: 8 year: 2013 end-page: 1837 ident: CR28 article-title: A review on multi-label learning algorithms publication-title: IEEE Trans. Knowl. Data Eng. – volume: 9 start-page: 571 year: 1980 end-page: 595 ident: CR45 article-title: Approximations of the critical region of the Friedman statistic publication-title: Commun. Stat. – volume: 17 start-page: 261 issue: 3 year: 2020 end-page: 272 ident: CR41 article-title: SciPy 1.0: Fundamental algorithms for scientific computing in Python publication-title: Nat. Methods – volume: 132 year: 2021 ident: CR12 article-title: Evaluation of machine learning algorithms for health and wellness applications: a tutorial publication-title: Comput. Biol. Med. – year: 2009 ident: CR38 publication-title: Python 3 Reference Manual – ident: CR8 – volume: 20 start-page: 37 issue: 1 year: 1960 end-page: 46 ident: CR20 article-title: A coefficient of agreement for nominal scales publication-title: Educ. Psychol. Meas. – ident: CR58 – volume: 13 start-page: 10528 year: 2023 ident: CR65 article-title: Classification of head and neck cancer from PET images using convolutional neural networks publication-title: Sci. Rep. – year: 2019 ident: CR10 publication-title: Hands-On Computer Vision with TensorFlow 2: Leverage Deep Learning to Create Powerful Image Processing Apps with TensorFlow 2.0 and Keras – volume: 44 start-page: 837 issue: 3 year: 1988 end-page: 45 ident: CR49 article-title: Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach publication-title: Biometrics – volume: 11 start-page: 37 issue: 2 year: 1912 end-page: 50 ident: CR33 article-title: The Distribution of the Flora in the Alpine Zone.1 publication-title: New Phytol. – year: 2021 ident: CR39 publication-title: R: A Language and Environment for Statistical Computing – year: 2019 ident: CR57 publication-title: An R Companion to Applied Regression – year: 1995 ident: CR53 publication-title: Statistical Methods for Engineers and Scientists – volume: 68 start-page: 77 issue: 1 year: 2008 end-page: 80 ident: CR47 article-title: McNemar chi2 test revisited: Comparing sensitivity and specificity of diagnostic examinations publication-title: Scand. J. Clin. Lab Invest. – ident: CR36 – volume: 3 start-page: 32 issue: 1 year: 1950 end-page: 35 ident: CR18 article-title: Index for rating diagnostic tests publication-title: Cancer – ident: CR5 – volume: 160 start-page: 268 year: 1937 end-page: 282 ident: CR55 article-title: Properties of sufficiency and statistical tests publication-title: Proc. R. Stat. Soc. Ser. A – ident: CR26 – volume: 1 start-page: 317 year: 1997 end-page: 328 ident: CR44 article-title: On comparing classifiers: Pitfalls to avoid and a recommended approach publication-title: Data Min. Knowl. Discov. – volume: 21 start-page: 1488 issue: 4 year: 2011 end-page: 1499 ident: CR35 article-title: On the mathematical properties of the structural similarity index publication-title: IEEE Trans. Image Process. – volume: 53 start-page: 1385 issue: 2 year: 2020 end-page: 1390 ident: CR2 article-title: Early history of machine learning publication-title: IFAC-PapersOnLine – volume: 175 year: 2021 ident: CR3 article-title: Machine Learning for industrial applications: A comprehensive literature review publication-title: Expert Syst. Appl. – volume: 7 start-page: 1 year: 2006 end-page: 30 ident: CR4 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: J. Mach. Learn. Res. – volume: 5 start-page: 1 issue: 4 year: 1948 end-page: 34 ident: CR31 article-title: A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons publication-title: K. Dan. Vidensk. Selsk. – year: 2012 ident: CR14 publication-title: Statistical and Machine Learning Approaches for Network Analysis – year: 2022 ident: CR16 publication-title: Laboratory Screening and Diagnostic Evaluation: An Evidence-Based Approach – volume: 26 start-page: 142 issue: 1 year: 2017 end-page: 154 ident: CR46 article-title: Does McNemar’s test compare the sensitivities and specificities of two diagnostic tests? publication-title: Stat. Methods Med. Res. – year: 2023 ident: CR17 publication-title: Statistics for Applied Behavior Analysis Practitioners and Researchers – volume: 132 year: 2021 ident: CR62 article-title: Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images publication-title: Comput. Biol. Med. – volume: 52 start-page: 591 issue: 3–4 year: 1965 end-page: 611 ident: CR54 article-title: An analysis of variance test for normality (complete samples) publication-title: Biometrika – year: 2007 ident: CR40 publication-title: Epidemiology, Biostatistics, and Preventive Medicine – volume: 134 year: 2023 ident: CR27 article-title: Weighted kappa measures for ordinal multi-class classification performance publication-title: Appl. Soft Comput. – volume: 12 issue: 6 year: 2017 ident: CR22 article-title: Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric publication-title: PLoS ONE – volume: 18 start-page: 49 year: 2013 end-page: 62 ident: CR37 article-title: Model based comparison of discounted cumulative gain and average precision publication-title: J. Discrete Algorithms – volume: 349 start-page: 255 issue: 6245 year: 2015 end-page: 260 ident: CR1 article-title: Machine learning: Trends, perspectives, and prospects publication-title: Science – year: 2023 ident: CR68 article-title: New method of using a convolutional neural network for 2D intraprostatic tumor segmentation from PET images publication-title: Res. Biomed. Eng. doi: 10.1007/s42600-023-00314-7 – volume: 7 year: 2019 ident: CR6 article-title: Lymph node detection in MR Lymphography: False positive reduction using multi-view convolutional neural networks publication-title: PeerJ – year: 2023 ident: CR66 article-title: Automatic segmentation of head and neck cancer from PET-MRI data using deep learning publication-title: J. Med. Biol. Eng. doi: 10.1007/s40846-023-00818-8 – volume: 44 start-page: 467 year: 2015 end-page: 508 ident: CR11 article-title: Dealing with the evaluation of supervised classification algorithms publication-title: Artif. Intell. Rev. – volume: 36 start-page: 1885 issue: 4 year: 2023 ident: CR60 article-title: Carimas: An extensive medical imaging data processing tool for research publication-title: J. Digit. Imaging – ident: CR48 – volume: 9 start-page: 1 year: 2009 ident: CR23 article-title: Estimation and comparison of receiver operating characteristic curves publication-title: Stata J. – volume: 8 start-page: 191586 year: 2020 end-page: 191601 ident: CR64 article-title: Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization publication-title: IEEE Access – volume: 19 start-page: 203 issue: 4 year: 2009 end-page: 211 ident: CR15 article-title: Measures of diagnostic accuracy: Basic definitions publication-title: EJIFCC – start-page: 278 year: 1960 end-page: 292 ident: CR56 article-title: Robust tests for equality of variances publication-title: Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling – ident: CR13 – volume: 2020 start-page: 132665 issue: 8 year: 2020 end-page: 132676 ident: CR61 article-title: Can AI help in screening Viral and COVID-19 pneumonia? publication-title: IEEE Access – ident: CR34 – year: 2006 ident: CR42 publication-title: How to Report Statistics in Medicine: Annotated Guidelines for Authors, Editors, and Reviewers – volume: 28 start-page: 3866 year: 2016 end-page: 3878 ident: CR29 article-title: Using Spearman’s correlation coefficients for exploratory data analysis on big dataset publication-title: Concurr. Comput. Pract. Exp. – year: 2023 ident: CR51 publication-title: ROC Analysis for Classification and Prediction in Practice – volume: 12 start-page: 77 year: 2011 ident: CR52 article-title: pROC: an open-source package for R and S+ to analyze and compare ROC curves publication-title: BMC Bioinform. – volume: 172 start-page: 1122 issue: 5 year: 2018 end-page: 1131.e9 ident: CR63 article-title: Identifying medical diagnoses and treatable diseases by image-based deep learning publication-title: Cell – ident: CR7 – ident: CR59 – year: 2023 ident: CR19 publication-title: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R – ident: CR24 – volume: 26 start-page: 297 issue: 3 year: 1945 end-page: 302 ident: CR30 article-title: Measures of the amount of ecologic association between species publication-title: Ecology – volume: 175 year: 2021 ident: 56706_CR3 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.114820 – volume: 44 start-page: 837 issue: 3 year: 1988 ident: 56706_CR49 publication-title: Biometrics doi: 10.2307/2531595 – year: 2023 ident: 56706_CR68 publication-title: Res. Biomed. Eng. doi: 10.1007/s42600-023-00314-7 – ident: 56706_CR8 – volume: 52 start-page: 591 issue: 3–4 year: 1965 ident: 56706_CR54 publication-title: Biometrika doi: 10.1093/biomet/52.3-4.591 – volume: 2020 start-page: 132665 issue: 8 year: 2020 ident: 56706_CR61 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3010287 – volume-title: Machine Learning with R: Learn Techniques for Building and Improving Machine Learning Models, from Data Preparation to Model Tuning, Evaluation, and Working with Big Data year: 2023 ident: 56706_CR21 – volume-title: R: A Language and Environment for Statistical Computing year: 2021 ident: 56706_CR39 – volume: 21 start-page: 1488 issue: 4 year: 2011 ident: 56706_CR35 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2011.2173206 – volume: 33 start-page: 6999 issue: 12 year: 2021 ident: 56706_CR9 publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2021.3084827 – volume: 7 start-page: 1 year: 2006 ident: 56706_CR4 publication-title: J. Mach. Learn. Res. – volume: 7 year: 2019 ident: 56706_CR6 publication-title: PeerJ doi: 10.7717/peerj.8052 – volume: 160 start-page: 268 year: 1937 ident: 56706_CR55 publication-title: Proc. R. Stat. Soc. Ser. A – ident: 56706_CR5 doi: 10.1007/978-981-16-6265-2_3 – volume-title: Epidemiology, Biostatistics, and Preventive Medicine year: 2007 ident: 56706_CR40 – volume-title: Statistics for Applied Behavior Analysis Practitioners and Researchers year: 2023 ident: 56706_CR17 – volume: 20 start-page: 37 issue: 1 year: 1960 ident: 56706_CR20 publication-title: Educ. Psychol. Meas. doi: 10.1177/001316446002000104 – volume-title: Foundations of Statistical Natural Language Processing year: 1999 ident: 56706_CR25 – volume: 17 start-page: 261 issue: 3 year: 2020 ident: 56706_CR41 publication-title: Nat. Methods doi: 10.1038/s41592-019-0686-2 – ident: 56706_CR59 – ident: 56706_CR34 – volume: 349 start-page: 255 issue: 6245 year: 2015 ident: 56706_CR1 publication-title: Science doi: 10.1126/science.aaa8415 – volume: 28 start-page: 3866 year: 2016 ident: 56706_CR29 publication-title: Concurr. Comput. Pract. Exp. doi: 10.1002/cpe.3745 – ident: 56706_CR36 doi: 10.1145/1148170.1148262 – volume: 9 start-page: 571 year: 1980 ident: 56706_CR45 publication-title: Commun. Stat. doi: 10.1080/03610928008827904 – volume: 44 start-page: 467 year: 2015 ident: 56706_CR11 publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-015-9433-y – ident: 56706_CR13 – volume: 12 start-page: 77 year: 2011 ident: 56706_CR52 publication-title: BMC Bioinform. doi: 10.1186/1471-2105-12-77 – volume: 18 start-page: 49 year: 2013 ident: 56706_CR37 publication-title: J. Discrete Algorithms doi: 10.1016/j.jda.2012.10.002 – volume: 172 start-page: 1122 issue: 5 year: 2018 ident: 56706_CR63 publication-title: Cell doi: 10.1016/j.cell.2018.02.010 – volume: 8 start-page: 191586 year: 2020 ident: 56706_CR64 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3031384 – start-page: 234 volume-title: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. MICCAI 2015 year: 2015 ident: 56706_CR67 doi: 10.1007/978-3-319-24574-4_28 – ident: 56706_CR7 doi: 10.1109/IEMBS.2003.1279826 – volume-title: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R year: 2023 ident: 56706_CR19 doi: 10.1007/978-3-031-13339-8 – volume-title: Hands-On Computer Vision with TensorFlow 2: Leverage Deep Learning to Create Powerful Image Processing Apps with TensorFlow 2.0 and Keras year: 2019 ident: 56706_CR10 – volume: 134 year: 2023 ident: 56706_CR27 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2023.110020 – volume: 26 start-page: 1819 issue: 8 year: 2013 ident: 56706_CR28 publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2013.39 – ident: 56706_CR58 – volume-title: ROC Analysis for Classification and Prediction in Practice year: 2023 ident: 56706_CR51 doi: 10.1201/9780429170140 – volume-title: Python 3 Reference Manual year: 2009 ident: 56706_CR38 – volume-title: How to Report Statistics in Medicine: Annotated Guidelines for Authors, Editors, and Reviewers year: 2006 ident: 56706_CR42 – ident: 56706_CR24 doi: 10.1007/978-3-030-12939-2_43 – volume: 1 start-page: 317 year: 1997 ident: 56706_CR44 publication-title: Data Min. Knowl. Discov. doi: 10.1023/A:1009752403260 – volume-title: Nonparametric Statistics for Non-statisticians year: 2009 ident: 56706_CR43 doi: 10.1002/9781118165881 – volume: 12 issue: 6 year: 2017 ident: 56706_CR22 publication-title: PLoS ONE doi: 10.1371/journal.pone.0177678 – volume: 36 start-page: 1885 issue: 4 year: 2023 ident: 56706_CR60 publication-title: J. Digit. Imaging doi: 10.1007/s10278-023-00812-1 – volume-title: An R Companion to Applied Regression year: 2019 ident: 56706_CR57 – volume: 26 start-page: 297 issue: 3 year: 1945 ident: 56706_CR30 publication-title: Ecology doi: 10.2307/1932409 – volume: 13 start-page: 10528 year: 2023 ident: 56706_CR65 publication-title: Sci. Rep. doi: 10.1038/s41598-023-37603-1 – volume: 11 start-page: 37 issue: 2 year: 1912 ident: 56706_CR33 publication-title: New Phytol. doi: 10.1111/j.1469-8137.1912.tb05611.x – ident: 56706_CR26 doi: 10.1007/978-3-319-05885-6_17 – start-page: 278 volume-title: Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling year: 1960 ident: 56706_CR56 – volume: 68 start-page: 77 issue: 1 year: 2008 ident: 56706_CR47 publication-title: Scand. J. Clin. Lab Invest. doi: 10.1080/00365510701666031 – volume: 17 start-page: 207 issue: 2 year: 2008 ident: 56706_CR50 publication-title: Stat. Methods Med. Res. doi: 10.1177/0962280207087173 – volume: 132 year: 2021 ident: 56706_CR62 publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2021.104319 – year: 2023 ident: 56706_CR66 publication-title: J. Med. Biol. Eng. doi: 10.1007/s40846-023-00818-8 – volume: 19 start-page: 203 issue: 4 year: 2009 ident: 56706_CR15 publication-title: EJIFCC – volume: 132 year: 2021 ident: 56706_CR12 publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2021.104324 – start-page: 309 volume-title: Biomedical Signal and Image Processing with Artificial Intelligence year: 2022 ident: 56706_CR32 – volume-title: Statistical Methods for Engineers and Scientists year: 1995 ident: 56706_CR53 – volume: 3 start-page: 32 issue: 1 year: 1950 ident: 56706_CR18 publication-title: Cancer doi: 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3 – volume: 26 start-page: 142 issue: 1 year: 2017 ident: 56706_CR46 publication-title: Stat. Methods Med. Res. doi: 10.1177/0962280214541852 – volume-title: Statistical and Machine Learning Approaches for Network Analysis year: 2012 ident: 56706_CR14 doi: 10.1002/9781118346990 – volume: 5 start-page: 1 issue: 4 year: 1948 ident: 56706_CR31 publication-title: K. Dan. Vidensk. Selsk. – volume-title: Laboratory Screening and Diagnostic Evaluation: An Evidence-Based Approach year: 2022 ident: 56706_CR16 – volume: 9 start-page: 1 year: 2009 ident: 56706_CR23 publication-title: Stata J. doi: 10.1177/1536867X0900900101 – ident: 56706_CR48 doi: 10.25080/Majora-92bf1922-011 – volume: 53 start-page: 1385 issue: 2 year: 2020 ident: 56706_CR2 publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2020.12.1888 – reference: 38977765 - Sci Rep. 2024 Jul 8;14(1):15724. doi: 10.1038/s41598-024-66611-y |
| SSID | ssj0000529419 |
| Score | 2.751196 |
| Snippet | Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with... Abstract Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 6086 |
| SubjectTerms | 639/705/117 639/705/531 Accuracy Bronchopulmonary infection Classification Evaluation metrics Humanities and Social Sciences Image processing Image Processing, Computer-Assisted - methods Information processing Learning algorithms Lung cancer Machine Learning Mathematical models Medical images multidisciplinary Neural networks Neural Networks, Computer Performance evaluation Positron emission tomography Science Science (multidisciplinary) Statistical analysis Statistical testing Supervised Machine Learning Tomography X-rays |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NSx0xEB9EFHop2mq7fpQt9FYXN5-bHFUUT9JDC95CPttCXcX3FP3vnWT3vfpsay9edxMYJjOZ35Dk9wP4ZHUbNXG8EcqHJrO_NNZTbFYol85b4ZK1RWyiOztT5-f6yyOpr3wnbKAHHhy3r7qEFZtrkoLjzGF_QLSIVrkuKYnJmXdfRD2PmqmB1ZtqTvT4SqZlan-ClSq_JqNoluywjb5bqESFsP9vKPPPy5JPTkxLITpZg9cjgqwPBsvXYSn2b2B10JS8fwsZG4_83fVFlsvyk9r2oc4vhwopM85FeDmd1AhX64tylzLWo3jE9w34dnL89ei0GTUSGi84meL-0AUvrWBC6tDK4GKSLimduKQac02EkByLQlgSLWUhUUGl5z6EoFuH6G4TlvvLPr6HGr3MMuRSRHiuY6cJQ_ituCdJea1DBWTmL-NHAvGsY_HLlINspszgY4M-NsXH5q6Cz_M5VwN9xrOjD_MyzEdm6uvyAQPCjAFh_hcQFezMFtGM-TgxFNvArGSj2go-zn9jJuXjEdvHy5syBptH2RFewbthzeeWMMVVi4W8ArUQDQumLv7pf_4obN2Zr6uTXFewNwuc33b92xdbL-GLbXhFc8Tn-4dsB5an1zdxF1b8LQbc9YeSMg_K8xjx priority: 102 providerName: Directory of Open Access Journals |
| Title | Evaluation metrics and statistical tests for machine learning |
| URI | https://link.springer.com/article/10.1038/s41598-024-56706-x https://www.ncbi.nlm.nih.gov/pubmed/38480847 https://www.proquest.com/docview/2956513380 https://www.proquest.com/docview/2957166714 https://pubmed.ncbi.nlm.nih.gov/PMC10937649 https://doaj.org/article/87f441491fdb43b289195ea8b7f86add |
| Volume | 14 |
| WOSCitedRecordID | wos001185520800061&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database (ProQuest) customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database (ProQuest) customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB7RFqReeFMCZRUkbhA1Tvw8IYpawaGrCIG0nCzHdgoSzZbNFpV_z9jxbrU8euHiQ2xLjmfG_uwZfwPwwqjSK9LSgknrisD-Uhhb4WGlory1hrWdMTHZhJhO5WymmnThNqSwytWaGBdqN7fhjvygQiAfcpHI8vX59yJkjQre1ZRCYwt2AktCHUP3mvUdS_BiUaLSW5mylgcD7lfhTVmFg-MCD9OXG_tRpO3_G9b8M2TyN79p3I6O7_zvj9yF2wmI5m9GzbkHN3x_H26NqSl_PoAAsRMNeH4Wsm7ZITe9y8MDpMjtjH0RpS6HHFFvfhZDMn2eclCcPoRPx0cf374rUqqFwjJKlrjMCGe5YTXjypXctb7jbSdVR3ml0GSZc11be8YM8aaqXVexiltqnXOqbBEkPoLtft77x5BTRGABuUnCLFVeKFIjipfUkk5apVwGZDXh2iYe8pAO45uO_vBa6lFIGoWko5D0ZQYv133ORxaOa1sfBjmuWwYG7fhhvjjVySC1FB0iQapI51pat3juJIp5I1vRSY6Lfgb7K_HpZNaDvpJdBs_X1WiQwctiej-_iG3wDMoFoRnsjUqzHkktqSwRD2QgN9RpY6ibNf3XL5H0O9B-CU5VBq9Wmnc1rn_PxZPrf-Mp7FbBGEKAYr0P28vFhX8GN-0PVKXFBLbETMRSTmDn8GjafJjESwssT6pmEq0Na5r3J83nX4o-Lcg |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB5VWxBceD8CBYIEJ4gaO7ZjHxDiVXXVdrWHIpWT69hOQaLZsruF9k_xGxk7yVbLo7ceuMaO5DjfjL_x2PMBPDMq94pULOPSuixUf8mMpRisUCYqa3hVGxPFJsrRSO7tqfEK_OzvwoRjlb1PjI7aTWzYI1-nSOSDFonMXx99y4JqVMiu9hIaLSy2_OkPDNlmr4bv8f8-p3Tjw-67zaxTFcgsZ2SOFlU6KwwvuFAuF67ytahqqWomqEJ0cufqqvCcG-INLVxNORWWWeecyisVCh2gy19lCHY5gNXxcGf8abGrE_JmjKjudk5eyPUZrpDhFhvF6RAlhu8nSytgFAr4G7v985Dmb5nauABuXP_fpu4GXOuodvqmtY2bsOKbW3C5Fd88vQ0hiOgKnaeHQVfMzlLTuDRcsYrVq_Fd5OHzWYq8Pj2Mh0592qlsHNyBjxcy9rswaCaNvw8pQ44ZuKkk3DLlS0UKjFMks6SWVimXAOl_sLZdpfUg-PFVx4x_IXULCo2g0BEU-iSBF4t3jto6I-f2fhtws-gZaoTHB5Ppge5cjpZljVyXKVK7ihUVRtZEcW9kVdZS4LKWwFoPF905rpk-w0oCTxfN6HJCHsk0fnIc-2CULUrCErjXgnQxkkIymSPjSUAuwXdpqMstzZfPsax5KGxWCqYSeNkj_Wxc_56LB-d_xhO4srm7s623h6Oth3CVBkMMxzGLNRjMp8f-EVyy3xFW08edLaewf9E28AsTNobY |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB5V5SEuvAuBAkGCE0QbO7ZjHxACSkVVtNoDSL0Zx4-CRLNldwvtX-PXMXaSrZZHbz1wjR3Jcb4Zz9jj7wN4YlTpFWlYwaV1RWR_KYylmKxQJhpreBOMSWIT9Xgs9_bUZA1-DndhYlnl4BOTo3ZTG_fIRxQD-ahFIstR6MsiJlvbLw-_FVFBKp60DnIaHUR2_ckPTN_mL3a28F8_pXT77Yc374peYaCwnJEFWlftrDC84kK5UrjGB9EEqQITVCFSuXOhqTznhnhDKxcop8Iy65xTZaMi6QG6_wt1JC1PZYOT5f5OPEFjRPX3dMpKjua4Vsb7bBQnRtSYyB-vrIVJMuBvce6f5Zq_ndmmpXD72v88idfhah-A5686i7kBa769CZc6Sc6TWxBTi57-PD-IamN2npvW5fHiVeK0xncxOl_Mc4z284NUiurzXntj_zZ8PJexb8B6O239XcgZRp4xYpWEW6Z8rUiF2YtklgRplXIZkOFna9vzr0cZkK861QFUUncA0QgQnQCijzN4tnznsGMfObP364ihZc_IHJ4eTGf7undEWtYBI2CmSHANqxrMt4ni3simDlLgYpfB5gAd3buzuT7FTQaPl83oiOLpkmn99Cj1wdxb1IRlcKcD7HIklWSyxDgoA7kC5ZWhrra0Xz4nsvNId1YLpjJ4PqD-dFz_not7Z3_GI7iMwNfvd8a79-EKjTYZazSrTVhfzI78A7hovyOqZg-TUefw6bwN4Bdsx44X |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Evaluation+metrics+and+statistical+tests+for+machine+learning&rft.jtitle=Scientific+reports&rft.au=Rainio%2C+Oona&rft.au=Teuho%2C+Jarmo&rft.au=Kl%C3%A9n%2C+Riku&rft.date=2024-03-13&rft.pub=Nature+Publishing+Group&rft.eissn=2045-2322&rft.volume=14&rft.issue=1&rft.spage=6086&rft_id=info:doi/10.1038%2Fs41598-024-56706-x&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |