<ul><li>Training a machine learning model involves showing it examples and adjusting its parameters to find patterns.</li><li>Evaluating the model using the same data it learned from is misleading as it may have just memorized the training examples.</li><li>To evaluate the model properly, a train/test split is used to measure its generalization ability on new, unseen data.</li><li>Overfitting can occur when the model becomes too complex and fits the noise and specifics of the training set instead of the underlying signal.</li></ul>

Don’t Trust Your Training Score: The Importance of Testing and Generalization

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