Difference between revisions of "Evaluation - Measures"
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* [http://medium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b Performance Metrics for Classification problems in Machine Learning | Mohammed Sunasra = Medium] | * [http://medium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b Performance Metrics for Classification problems in Machine Learning | Mohammed Sunasra = Medium] | ||
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=== Accuracy === | === Accuracy === | ||
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| + | === Precision & Recall === | ||
| + | [http://www.youtube.com/results?search_query=Precision+Recall+artificial+intelligence YouTube search...] | ||
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| + | (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that have been retrieved over the total amount of relevant instances. Both precision and recall are therefore based on an understanding and measure of relevance. [http://en.wikipedia.org/wiki/Precision_and_recall Precision and recall | Wikipedia] | ||
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| + | http://upload.wikimedia.org/wikipedia/commons/thumb/2/26/Precisionrecall.svg/525px-Precisionrecall.svg.png | ||
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=== F1 Score (F-Measure) === | === F1 Score (F-Measure) === | ||
Revision as of 12:10, 22 September 2018
Contents
Error Metric
Predictive Modeling works on constructive feedback principle. You build a model. Get feedback from metrics, make improvements and continue until you achieve a desirable accuracy. Evaluation metrics explain the performance of a model. An important aspects of evaluation metrics is their capability to discriminate among model results. 7 Important Model Evaluation Error Metrics Everyone should know | Tavish Srivastava
Confusion Matrix
A performance measurement for machine learning classification Understanding Confusion Matrix | Sarang Narkhede - Medium
Accuracy
The number of correct predictions made by the model over all kinds predictions made.
Precision & Recall
(also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that have been retrieved over the total amount of relevant instances. Both precision and recall are therefore based on an understanding and measure of relevance. Precision and recall | Wikipedia
F1 Score (F-Measure)
Receiver Operator Curves (ROC) and Area Under the Curve (AUC)
Example Use: Tradeoffs
'Sensitivity' & 'Specificity':
'True Positive Rate' & 'False Positive Rate':
