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This code snippet uses `plot_tree` from `sklearn.tree` to generate a visual representation of our trained **regression tree**. We specify the `feature_names` so the splits are labeled clearly. The `filled=True` argument colors the nodes based on their prediction value, and `rounded=True` gives the nodes rounded corners for a cleaner look. `fontsize` adjusts the text size within the nodes. Displaying this plot allows you to see exactly how the algorithm decided to split the data at each step. You can trace a path from the root node down to a leaf node for any given data point to understand how its prediction was derived. For example, you might see a split like 'SquareFootage <= 1750'. If a house has 1500 sqft, it goes down the left branch; if it has 2000 sqft, it goes down the right. Each node typically shows the criterion for the split, the number of samples that reached that node, and the average target value (or MSE) for those samples. Leaf nodes will simply show the average predicted value for the samples that ended up there. This visualization is fantastic for debugging your model, understanding feature importance implicitly, and explaining the model's behavior to others who might not be machine learning experts. It truly brings the 'tree' metaphor to life and is a powerful tool for understanding your **regression tree**'s logic.
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5. **Set Up Your TV Again:** After the reset, your TV will restart and go through the initial setup process. Follow the prompts to set up your channels, network, and other preferences.
