Score

Accuracy Score

$$accuracy = \frac{\text{correct classifications}}{\text{all classifications}}$$

from sklearn.metrics import accuracy_score
y_true = [0, 1, 2, 0, 2, 1]
y_pred = [0, 2, 2, 0, 1, 1]
accuracy_score(y_true, y_pred)

Confusion matrix

from sklearn.metrics import confusion_matrix
confusion_matrix(y_true, y_pred)

Balanced Accuracy Score

from sklearn.metrics import balanced_accuracy_score
balanced_accuracy_score(y_true, y_pred)

R² Score

Coefficient of determination

$$R^2=1-\frac{u}{v}$$

$$R^2=1-\frac{SS_{rediduals}}{SS_{total}}$$

Residual sum of squares

$$u = \sum{(y_{true}-y_{pred})^2}$$

Total sum of squares

$$v = \sum{(y_{true}-\bar{y}_{true})^2}$$

from sklearn.metrics import r2_score
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
r2_score(y_true, y_pred)

Adjusted R squared

$$\text{Adjusted }R^2=1-\frac{SS_{residuals}\dot (n-1)}{SS_{total} \dot (n-k)}$$

Standard Error

$$SE = \sqrt{\frac{\sum{(y_i-\hat{y}_i)^2}}{n-(k-1)}}$$

Reference