11 docs tagged with "model-evaluation"

Understanding the most common evaluation metric, its formula, and its fatal flaws in imbalanced datasets.

Mastering the harmonic mean of Precision and Recall to evaluate models on imbalanced datasets.

Mastering robust model evaluation by rotating training and testing sets to maximize data utility.

The most exhaustive validation technique: training on N-1 samples and testing on a single observation.

Understanding cross-entropy loss and why it is the gold standard for evaluating probability-based classifiers.

Understanding Precision, its mathematical foundation, and why it is vital for minimizing False Positives.

Understanding Recall, its mathematical definition, and why it is critical for minimizing False Negatives.

Evaluating classifier performance across all thresholds using the Receiver Operating Characteristic and Area Under the Curve.

The foundation of classification evaluation: True Positives, False Positives, True Negatives, and False Negatives.

Mastering the data partitioning process to ensure unbiased model evaluation.

Understanding the difference between training performance and real-world reliability.