{"id":410147,"date":"2024-10-20T05:39:01","date_gmt":"2024-10-20T05:39:01","guid":{"rendered":"https:\/\/pdfstandards.shop\/product\/uncategorized\/bsi-pd-iso-iec-ts-42132022\/"},"modified":"2024-10-26T10:23:18","modified_gmt":"2024-10-26T10:23:18","slug":"bsi-pd-iso-iec-ts-42132022","status":"publish","type":"product","link":"https:\/\/pdfstandards.shop\/product\/publishers\/bsi\/bsi-pd-iso-iec-ts-42132022\/","title":{"rendered":"BSI PD ISO\/IEC\/TS 4213:2022"},"content":{"rendered":"

PDF Catalog<\/h4>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
PDF Pages<\/th>\nPDF Title<\/th>\n<\/tr>\n
2<\/td>\nNational foreword <\/td>\n<\/tr>\n
7<\/td>\nForeword <\/td>\n<\/tr>\n
8<\/td>\nIntroduction <\/td>\n<\/tr>\n
9<\/td>\n1 Scope
2 Normative references
3 Terms and definitions
3.1 Classification and related terms
3.2 Metrics and related terms <\/td>\n<\/tr>\n
11<\/td>\n4 Abbreviated terms <\/td>\n<\/tr>\n
12<\/td>\n5 General principles
5.1 Generalized process for machine learning classification performance assessment
5.2 Purpose of machine learning classification performance assessment <\/td>\n<\/tr>\n
13<\/td>\n5.3 Control criteria in machine learning classification performance assessment
5.3.1 General
5.3.2 Data representativeness and bias
5.3.3 Preprocessing
5.3.4 Training data <\/td>\n<\/tr>\n
14<\/td>\n5.3.5 Test and validation data
5.3.6 Cross-validation
5.3.7 Limiting information leakage
5.3.8 Limiting channel effects <\/td>\n<\/tr>\n
15<\/td>\n5.3.9 Ground truth
5.3.10 Machine learning algorithms, hyperparameters and parameters <\/td>\n<\/tr>\n
16<\/td>\n5.3.11 Evaluation environment
5.3.12 Acceleration
5.3.13 Appropriate baselines
5.3.14 Machine learning classification performance context
6 Statistical measures of performance
6.1 General <\/td>\n<\/tr>\n
17<\/td>\n6.2 Base elements for metric computation
6.2.1 General
6.2.2 Confusion matrix
6.2.3 Accuracy
6.2.4 Precision, recall and specificity
6.2.5 F1 score <\/td>\n<\/tr>\n
18<\/td>\n6.2.6 F\u03b2
6.2.7 Kullback-Leibler divergence
6.3 Binary classification
6.3.1 General <\/td>\n<\/tr>\n
19<\/td>\n6.3.2 Confusion matrix for binary classification
6.3.3 Accuracy for binary classification
6.3.4 Precision, recall, specificity, F1 score and F\u03b2 for binary classification
6.3.5 Kullback-Leibler divergence for binary classification
6.3.6 Receiver operating characteristic curve and area under the receiver operating characteristic curve <\/td>\n<\/tr>\n
20<\/td>\n6.3.7 Precision recall curve and area under the precision recall curve
6.3.8 Cumulative response curve
6.3.9 Lift curve
6.4 Multi-class classification
6.4.1 General
6.4.2 Accuracy for multi-class classification
6.4.3 Macro-average, weighted-average and micro-average <\/td>\n<\/tr>\n
22<\/td>\n6.4.4 Distribution difference or distance metrics
6.5 Multi-label classification
6.5.1 General
6.5.2 Hamming loss <\/td>\n<\/tr>\n
23<\/td>\n6.5.3 Exact match ratio
6.5.4 Jaccard index <\/td>\n<\/tr>\n
24<\/td>\n6.5.5 Distribution difference or distance metrics
6.6 Computational complexity
6.6.1 General
6.6.2 Classification latency <\/td>\n<\/tr>\n
25<\/td>\n6.6.3 Classification throughput
6.6.4 Classification efficiency
6.6.5 Energy consumption <\/td>\n<\/tr>\n
26<\/td>\n7 Statistical tests of significance
7.1 General <\/td>\n<\/tr>\n
27<\/td>\n7.2 Paired Student\u2019s t-test
7.3 Analysis of variance
7.4 Kruskal-Wallis test
7.5 Chi-squared test
7.6 Wilcoxon signed-ranks test <\/td>\n<\/tr>\n
28<\/td>\n7.7 Fisher\u2019s exact test
7.8 Central limit theorem
7.9 McNemar test
7.10 Accommodating multiple comparisons
7.10.1 General <\/td>\n<\/tr>\n
29<\/td>\n7.10.2 Bonferroni correction
7.10.3 False discovery rate
8 Reporting <\/td>\n<\/tr>\n
30<\/td>\nAnnex A (informative) Multi-class classification performance illustration <\/td>\n<\/tr>\n
32<\/td>\nAnnex B (informative) Illustration of ROC curve derived from classification results <\/td>\n<\/tr>\n
37<\/td>\nAnnex C (informative) Summary information on machine learning classification benchmark tests <\/td>\n<\/tr>\n
39<\/td>\nAnnex D (informative) Chance-corrected cause-specific mortality fraction <\/td>\n<\/tr>\n
40<\/td>\nBibliography <\/td>\n<\/tr>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

Information technology. Artificial Intelligence. Assessment of machine learning classification performance<\/b><\/p>\n\n\n\n\n
Published By<\/td>\nPublication Date<\/td>\nNumber of Pages<\/td>\n<\/tr>\n
BSI<\/b><\/a><\/td>\n2022<\/td>\n42<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"featured_media":410152,"template":"","meta":{"rank_math_lock_modified_date":false,"ep_exclude_from_search":false},"product_cat":[2641],"product_tag":[],"class_list":{"0":"post-410147","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-bsi","8":"first","9":"instock","10":"sold-individually","11":"shipping-taxable","12":"purchasable","13":"product-type-simple"},"_links":{"self":[{"href":"https:\/\/pdfstandards.shop\/wp-json\/wp\/v2\/product\/410147","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pdfstandards.shop\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/pdfstandards.shop\/wp-json\/wp\/v2\/types\/product"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pdfstandards.shop\/wp-json\/wp\/v2\/media\/410152"}],"wp:attachment":[{"href":"https:\/\/pdfstandards.shop\/wp-json\/wp\/v2\/media?parent=410147"}],"wp:term":[{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/pdfstandards.shop\/wp-json\/wp\/v2\/product_cat?post=410147"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/pdfstandards.shop\/wp-json\/wp\/v2\/product_tag?post=410147"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}