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End to End Project - Bikes Assessment - Basic - Train and Analyze the Models - Train DecisionTree Model

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.

INSTRUCTIONS
  • 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)
    

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