What are Performance parameters in Machine Learning (Important topic for ML)?
Performance Parameters in Machine Learning Models performance parameters (evaluation metrics) is essential for understanding how well a Machine Learning model performs. Here’s a complete explanation of the most important performance parameters for ML models, explained simply with examples and formulas. 1. Accuracy Accuracy measures the percentage of correctly predicted instances out of the total predictions. Accuracy = \frac{TP + TN}{TP + TN + FP + FN} Where: TP = True Positive TN = True Negative FP = False Positive FN = False Negative Example: If your model correctly predicts 90 out of 100 results, Accuracy = 90%. Best for balanced datasets. Performance Parameters 2. Precision Precision tells how many of the predicted positive results are actually correct. Precision = \frac{TP}{TP + FP} Example: Out of 100 “spam” predictions, if 90 are truly spam → Precision = 90%. Good when the cost of false positives is high. Used in emai...