Model Evaluation in Binary Classification
Explore the importance of model evaluation in binary classification, including concepts like confusion matrix, accuracy, precision, recall, and the trade-off between them. Learn how to interpret metrics for balanced and imbalanced datasets to enhance your machine learning models.
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ML Model Evaluation for Binary Classification Ajitkumar S. Shitole Computer Engineering International Institute of Information Technology, I IT www.isquareit.edu.in
Confusion Matrix Total Testing Samples= 100 Total Positive samples= 70 Total Negative Samples= 30 Predicted Samples N P 25 (TN) 05 (FP) N Actual Samples P 10 (FN) 60 (TP) TP=True Positive, TN=True Negative, FP=False Positive, FN=False Negative International Institute of Information Technology, I IT, P-14, Rajiv Gandhi Infotech Park, Hinjawadi Phase 1, Pune - 411 057 Phone - +91 20 22933441/2/3 | Website - www.isquareit.edu.in | Email - info@isquareit.edu.in
Confusion Matrix Total Samples= 100, TN=25, TP=60, FP=05, FN=10 Accuracy= (TP+TN)/Total Samples = (60+25)/100=0.85 Balanced Dataset : Accuracy is a correct performance measurement technique. Accuracy is the proportion of total samples that are correctly identified by the Machine Learning (ML) model. Imbalanced Dataset: Accuracy mislead interpretation. Precision and Recall are preferred. Precision= TP/(TP+FP) Precision is the proportion of predicted positive samples that are correctly identified by the ML model. Recall= TP/(TP+FN) Recall is the proportion of actual positive samples that are correctly identified by the ML model. International Institute of Information Technology, I IT, P-14, Rajiv Gandhi Infotech Park, Hinjawadi Phase 1, Pune - 411 057 Phone - +91 20 22933441/2/3 | Website - www.isquareit.edu.in | Email - info@isquareit.edu.in
Confusion Matrix Total Samples= 100, TN=25, TP=60, FP=05, FN=10 Accuracy is also called as Recognition Rate. Misclassification Error = 1 Accuracy = 1 0.85= 0. 15 Precision = Positive Predictive Value (PPV) =TP/(TP+FP)=60/(60+05)=60/65 = 0.92 Recall = Sensitivity = True Positive Rate = TP/(TP+FN) = 60/(60+10) = 60/70 = 0.85 False Positives are also called as Type-I errors. False Negatives are also called as Type-II errors. Trade-off between Precision and Recall. One may also prefer Receiver Operating Characteristic Curve (ROC) to measure the performance of the model for imbalanced datasets. International Institute of Information Technology, I IT, P-14, Rajiv Gandhi Infotech Park, Hinjawadi Phase 1, Pune - 411 057 Phone - +91 20 22933441/2/3 | Website - www.isquareit.edu.in | Email - info@isquareit.edu.in
Ajitkumar Shitole Department of Computer Engineering International Institute of Information Technology P-14, Rajiv Gandhi Infotech Park, Hinjawadi Phase II Pune 411 057 www.isquareit.edu.in info@isquareit.edu.in | admission@isquareit.edu.in https://www.youtube.com/channel/UCtcG-111OkwGLKy8KUqJvxQ https://twitter.com/isquareit_pune https://www.facebook.com/I2ITPune https://www.linkedin.com/company/i2it_2 International Institute of Information Technology, I IT, P-14, Rajiv Gandhi Infotech Park, Hinjawadi Phase 1, Pune - 411 057 Phone - +91 20 22933441/2/3 | Website - www.isquareit.edu.in | Email - info@isquareit.edu.in
