Precision and recall are metrics for classification machine learning models. Recall is the model's ability to capture positive cases and precision is the accuracy of the cases that it does capture.
The confusion matrix is a set of four values which show the performance of classification machine learning models per class. These four values are True Positive, False Positive, True Negative, and False Negative.
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.
MAE (Mean Absolute Error) is a common metric to use for measuring the error of regression predictions. Use this calculator to calculate the MAE for a list of predictions and their corresponding actual values.
The Coefficient of Determination, often called R Squared, is a metric that measures a model's goodness of fit, and the Correlation Coefficient (often called R) is the correlation between the two sets of values. This calculator will calculate both these metrics for two data lists.