Ml Evaluation Metrics — Free Machine Learning Tutorial
Learn Ml Evaluation Metrics in Machine Learning with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ml Evaluation Metrics in Machine Learning with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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Ml Evaluation Metrics in Machine Learning
For classification, accuracy (% correct) is intuitive but misleading for imbalanced data. A cancer detector with 99% accuracy but 0% recall (misses all actual cancers) is useless. Precision (TP / (TP + FP)) answers "when we predict positive, how often correct?" Recall (TP / (TP + FN)) answers "of all actual positives, how many did we catch?" True positive (TP) = correctly predicted positive, false positive (FP) = incorrectly predicted positive, false negative (FN) = incorrectly predicted negative.
F1 score (harmonic mean of precision and recall) balances both. For imbalanced data, use precision-recall curves and AUC-ROC (area under the receiver operating characteristic curve). AUC measures the model's ability to rank positives higher than negatives. For regression, use RMSE (penalizes large errors), MAE (average absolute error, robust to outliers), and R² (proportion of variance explained).
Confusion matrix visualizes: (TP, FP) in top row (predicted positive), (FN, TN) in bottom row (predicted negative). For multi-class, report macro-average (unweighted average) or weighted average (weighted by class frequency). Cross-validation ensures metrics are stable, not flukes.
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