Train the Decision Tree Model on the 'Training' data set using cross-validation and calculate 'mean absolute error' and 'root mean squared error' (RMSE) for this model.
Display these scores using display_scores() function.
Create a DecisionTreeRegressor instance, called dec_reg by passing random seed of 42 to the DecisionTreeRegressor.
Call cross_val_score() function, to perform training and cross validation and to calculate the mean absolute error scores, by passing to it the following:
DecisionTreeRegressor object dec_reg
trainingCols dataframe
trainingLabels dataframe
parameter cv with value 10 (cv=10)
scoring parameter with value "neg_mean_absolute_error"
dt_mae_scores = -cross_val_score(<<your code comes here>>)
display_scores(dt_mae_scores)
Call cross_val_score() function, to perform training and cross validation and to calculate the mean squared error scores, by passing to it the following:
DecisionTreeRegressor object dec_reg
trainingCols dataframe
trainingLabels dataframe
parameter cv with value 10 (cv=10)
scoring parameter with value "neg_mean_squared_error"
dt_mse_scores = np.sqrt(-cross_val_score(<<your code comes here>>))
display_scores(dt_mse_scores)
Note - Having trouble with the assessment engine? Follow the steps listed here
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