# Confusion Matrix Calculator (simple to use)

The confusion matrix is a method of measuring the performance of classification machine learning models using the True Positive, False Positive, True Negative, and False Negative values. These four values can be used to calculate a set of metrics that describe different aspects of model performance.

The confusion matrix is a method of measuring the performance of classification machine learning models using the True Positive, False Positive, True Negative, and False Negative values. These four values can be used to calculate a set of metrics that describe different aspects of model performance.

The metrics calculated in this calculator are **F1 score, accuracy, balanced accuracy, precision, recall, and sensitivity.**

This calculator will calculate these metrics using both typical confusion matrix values, but also from lists of predictions and their corresponding actual values.

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