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End to End ML Project - Train a Decision Tree model

Now that we have prepared the data, we will train a Decision Tree model on that data and see how it performs. Since this is a regression problem, we will use the DecisionTreeRegressor class from Scikit-learn.

  • Import the DecisionTreeRegressor class from Scikit-learn

    from sklearn.tree import <<your code goes here>>
  • Now let's train the DecisionTreeRegressor

    tree_reg = DecisionTreeRegressor(random_state=42), housing_labels)
  • To evaluate the performance of our model, we will import the mean_squared_error class from Scikit-learn

    from sklearn.metrics import <<your code goes here>>
  • Now let's predict using our model using the predict method

    housing_predictions = tree_reg.<<your code goes here>>(housing_prepared)
  • Finally, let's evaluate our model

    tree_mse = mean_squared_error(housing_labels, housing_predictions)
    tree_rmse = np.sqrt(tree_mse)

    If your trained your model correctly, the rmse would come to 0.0. This means that our model is overfitting. How to resolve this issue? We will come to that in a bit, but before that we will train a Random Forest model.

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