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8 results for “classification”

Formulae (8)

Accuracy = \frac{TP + TN}{TP + TN + FP + FN}

Fraction of predictions that are correct.

Precision = \frac{TP}{TP + FP}

Of all positive predictions, the fraction that were correct.

Recall = \frac{TP}{TP + FN}

Of all actual positives, the fraction correctly identified.

F_1 = 2\cdot\frac{Precision \cdot Recall}{Precision + Recall}

Harmonic mean of precision and recall.

Specificity = \frac{TN}{TN + FP}

Of all actual negatives, the fraction correctly identified.

\sigma(z_i) = \frac{e^{z_i}}{\sum_{j=1}^{K} e^{z_j}}

Converts a vector of scores into a probability distribution.

L = -\frac{1}{n}\sum[y\log(\hat{y}) + (1-y)\log(1-\hat{y})]

Loss function for binary classification.

L = -\sum_{i=1}^{K} y_i \log(\hat{y}_i)

Loss for multi-class classification with one-hot labels.