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.
Related articles
Metric comparisons
AUC vs accuracy
Accuracy vs balanced accuracy
F1 score vs AUC
F1 score vs accuracy
Micro vs Macro F1 score
Metric calculators
F1 score calculator
Accuracy calculator
Confusion matrix maker
Precision recall calculator
References
