Accuracy: The Intuitive Metric
Understanding the most common evaluation metric, its formula, and its fatal flaws in imbalanced datasets.
8 docs tagged with "metrics"
View all tagsF1-Score: The Balanced Metric
Mastering the harmonic mean of Precision and Recall to evaluate models on imbalanced datasets.
Log Loss (Logarithmic Loss): The Probability Penalty
Understanding cross-entropy loss and why it is the gold standard for evaluating probability-based classifiers.
Precision: The Quality Metric for Positive Predictions
Understanding Precision, its mathematical foundation, and why it is vital for minimizing False Positives.
Recall: The Sensitivity Metric
Understanding Recall, its mathematical definition, and why it is critical for minimizing False Negatives.
ROC Curve and AUC
Evaluating classifier performance across all thresholds using the Receiver Operating Characteristic and Area Under the Curve.
The Confusion Matrix
The foundation of classification evaluation: True Positives, False Positives, True Negatives, and False Negatives.
Why Model Evaluation Matters
Understanding the difference between training performance and real-world reliability.