Measuring how well your model performs and ensuring it generalizes to new data.
Splitting your data into training and testing sets to prevent overfitting.
A robust method for estimating model performance by training on multiple data splits.
Understanding accuracy, precision, recall, F1-score, and MAE.
A table that summarizes the performance of a classification model.