Classification Report

The classification_report function in sklearn.metrics is used to evaluate the performance of a classification model. It provides a summary of key metrics for each class, including precision, recall, F1-score, and support.

Function Signature

sklearn.metrics.classification_report(
    y_true, 
    y_pred, 
    , 
    labels=None, 
    target_names=None, 
    sample_weight=None, 
    digits=2, 
    output_dict=False, 
    zero_division='warn'
)

Parameters:

• y_true: Array of true labels.
• y_pred: Array of predicted labels.
• labels: (Optional) List of label indices to include in the report.
• target_names: (Optional) List of string names for the labels.
• sample_weight: (Optional) Array of weights for each sample.
• digits: Number of decimal places for formatting output.
• output_dict: If True, return output as a dictionary.
• zero_division: Sets the behavior when there is a zero division (e.g., ‘warn’, 0, 1).

Metrics Explained

• Precision: The ratio of correctly predicted positive observations to the total predicted positives. It indicates the quality of the positive class predictions.

• Recall (Sensitivity): The ratio of correctly predicted positive observations to all actual positives. It measures the ability of a model to find all relevant cases.

• F1 Score: The weighted average of precision and recall. It is a better measure than accuracy for imbalanced classes.

• Support: The number of actual occurrences of the class in the specified dataset.

Resources

In ML_Tools see: Evaluation_Metrics.py

official documentation.