🔥🔥 Don’t Miss Out on Our End of Autumn Sale. If you have any questions, feel free to click the bottom right corner to contact our customer service..

Concat us[email protected]
Shopping Cart

No products in the cart.

🔍
BSI PD ISO/IEC/TS 4213:2022

BSI PD ISO/IEC/TS 4213:2022

$167.15

Information technology. Artificial Intelligence. Assessment of machine learning classification performance

Published By Publication Date Number of Pages
BSI 2022 42
PDF Format Searchable Printable Preview Now
Guaranteed Safe Checkout
Category:

If you have any questions, feel free to reach out to our online customer service team by clicking on the bottom right corner. We’re here to assist you 24/7.
Email:[email protected]

PDF Catalog

PDF Pages PDF Title
2 National foreword
7 Foreword
8 Introduction
9 1 Scope
2 Normative references
3 Terms and definitions
3.1 Classification and related terms
3.2 Metrics and related terms
11 4 Abbreviated terms
12 5 General principles
5.1 Generalized process for machine learning classification performance assessment
5.2 Purpose of machine learning classification performance assessment
13 5.3 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
14 5.3.5 Test and validation data
5.3.6 Cross-validation
5.3.7 Limiting information leakage
5.3.8 Limiting channel effects
15 5.3.9 Ground truth
5.3.10 Machine learning algorithms, hyperparameters and parameters
16 5.3.11 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
17 6.2 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
18 6.2.6 Fβ
6.2.7 Kullback-Leibler divergence
6.3 Binary classification
6.3.1 General
19 6.3.2 Confusion matrix for binary classification
6.3.3 Accuracy for binary classification
6.3.4 Precision, recall, specificity, F1 score and Fβ 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
20 6.3.7 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
22 6.4.4 Distribution difference or distance metrics
6.5 Multi-label classification
6.5.1 General
6.5.2 Hamming loss
23 6.5.3 Exact match ratio
6.5.4 Jaccard index
24 6.5.5 Distribution difference or distance metrics
6.6 Computational complexity
6.6.1 General
6.6.2 Classification latency
25 6.6.3 Classification throughput
6.6.4 Classification efficiency
6.6.5 Energy consumption
26 7 Statistical tests of significance
7.1 General
27 7.2 Paired Student’s t-test
7.3 Analysis of variance
7.4 Kruskal-Wallis test
7.5 Chi-squared test
7.6 Wilcoxon signed-ranks test
28 7.7 Fisher’s exact test
7.8 Central limit theorem
7.9 McNemar test
7.10 Accommodating multiple comparisons
7.10.1 General
29 7.10.2 Bonferroni correction
7.10.3 False discovery rate
8 Reporting
30 Annex A (informative) Multi-class classification performance illustration
32 Annex B (informative) Illustration of ROC curve derived from classification results
37 Annex C (informative) Summary information on machine learning classification benchmark tests
39 Annex D (informative) Chance-corrected cause-specific mortality fraction
40 Bibliography

Related products

BSI PD ISO/IEC/TS 4213:2022
$167.15