riables.  

A model that performs well on the training dataset but poorly on the test dataset is said to be **overfit** because the model performed worse than expected when given new data. To some extent the patterns found in the training data may have been random noise and therefore, by random chance, are different in the test data.  

**Common performance metrics**: Accuracy, Mean squared error, Sensitivity and specificity, Area under the ROC curve (AUC)  

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# Workshop goals

### 1) Learn