Confusion Matrix

It provides a detailed breakdown of the model’s predictions, enabling a more comprehensive understanding of its performance. The confusion matrix is particularly useful when evaluating classification problems with multiple classes.

A typical confusion matrix has two dimensions: the rows represent the actual class labels, and the columns represent the predicted class labels. Each cell in the matrix represents the count or frequency of instances that fall into a specific combination of actual and predicted classes.

The confusion matrix provides insights into the model’s performance for each class individually and overall. It helps identify the types of errors made by the model, such as false positives and false negatives. By analyzing the confusion matrix, practitioners can gain a deeper understanding of the model’s strengths and weaknesses and make informed decisions for model improvement and optimization. The confusion matrix allows for the calculation of various evaluation metrics:

• True Positives (TP): The instances that are correctly predicted as positive (actual positive, predicted positive).
• True Negatives (TN): The instances that are correctly predicted as negative (actual negative, predicted negative).
• False Positives (FP): The instances that are incorrectly predicted as positive (actual negative, predicted positive).
• False Negatives (FN): The instances that are incorrectly predicted as negative (actual positive, predicted negative).

Classification evaluation metrics are quantitative measures used to assess the performance and accuracy of a classification model. These metrics provide insights into how well the model can classify instances into predefined classes or categories.

The commonly used classification evaluation metrics are: