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The magic happens with the `.fit(X_train, y_train)` method. This is where the **regression tree** algorithm analyzes our training features (`X_train`) and corresponding target values (`y_train`) to learn the underlying patterns and build the tree structure. The algorithm will find the best features and split points to minimize the prediction error (typically Mean Squared Error by default) across the training data. Once `fit` is called, our `regressor` object is trained and ready to make predictions. It's pretty amazing how concise this step is thanks to Scikit-learn. The `fit` method internally figures out the optimal splits based on the data and the chosen criterion (like MSE). Remember, the choice of `max_depth` (or other parameters like `min_samples_split`, `min_samples_leaf`) can significantly impact the model's performance. Experimenting with these hyperparameters is a key part of the model tuning process to find the sweet spot between underfitting (too simple) and overfitting (too complex).
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