classification metrics

1. classification_report

The classification_report function provides a comprehensive summary of precision, recall, F1 score, and support for each class. It can also include average metrics if specified.

from sklearn.metrics import classification_report

y_true = [0, 1, 1, 0, 1, 1]
y_pred = [0, 0, 1, 0, 1, 1]

report = classification_report(y_true, y_pred)
print(report)

2. confusion_matrix

The confusion_matrix function computes the confusion matrix, which can be used to derive other metrics manually.

from sklearn.metrics import confusion_matrix

y_true = [0, 1, 1, 0, 1, 1]
y_pred = [0, 0, 1, 0, 1, 1]

cm = confusion_matrix(y_true, y_pred)
print(cm)

3. balanced_accuracy_score

The balanced_accuracy_score function computes the balanced accuracy directly.

from sklearn.metrics import balanced_accuracy_score

y_true = [0, 1, 1, 0, 1, 1]
y_pred = [0, 0, 1, 0, 1, 1]

balanced_accuracy = balanced_accuracy_score(y_true, y_pred)
print(balanced_accuracy)

4. accuracy_score

The accuracy_score function computes the standard accuracy, which is the ratio of correctly predicted instances to the total instances.

from sklearn.metrics import accuracy_score

y_true = [0, 1, 1, 0, 1, 1]
y_pred = [0, 0, 1, 0, 1, 1]

accuracy = accuracy_score(y_true, y_pred)
print(accuracy)

5. precision_score, recall_score, f1_score

These functions calculate precision, recall, and F1 score individually, allowing for more fine-grained control over the calculations.

from sklearn.metrics import precision_score, recall_score, f1_score

y_true = [0, 1, 1, 0, 1, 1]
y_pred = [0, 0, 1, 0, 1, 1]

precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)

print(f"Precision: {precision}")
print(f"Recall: {recall}")
print(f"F1 Score: {f1}")

6. roc_auc_score

The roc_auc_score function computes the Area Under the Receiver Operating Characteristic Curve (ROC AUC), which is a performance measurement for classification problems at various threshold settings.

from sklearn.metrics import roc_auc_score

y_true = [0, 1, 1, 0, 1, 1]
y_pred_proba = [0.1, 0.4, 0.8, 0.2, 0.9, 0.7]  # Example probabilities

roc_auc = roc_auc_score(y_true, y_pred_proba)
print(roc_auc)

7. average_precision_score

The average_precision_score function calculates the average precision score, which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold.

from sklearn.metrics import average_precision_score

y_true = [0, 1, 1, 0, 1, 1]
y_pred_proba = [0.1, 0.4, 0.8, 0.2, 0.9, 0.7]

average_precision = average_precision_score(y_true, y_pred_proba)
print(average_precision)

8. fscoreSupport

# Calculating the Scores
vHatY                    = oSVM.predict(mX)
precision, recall, f1, support = precision_recall_fscore_support(vY, vHatY, pos_label = 1, average = 'binary')


print(f'Precision = {precision:0.3f}')
print(f'Recall    = {recall:0.3f}'   )
print(f'f1        = {f1:0.3f}'       )
print(f'Support   = {support}'  )