Branch-AI/one_exercise_per_file/week02/day04/ex03/audit/readme.md

1. This question is validated if the predictions on the train set and test set are:

    # 10 first values Train
    array([1.54505951, 2.21338527, 2.2636205 , 3.3258957 , 1.51710076,
        1.63209319, 2.9265211 , 0.78080924, 1.21968217, 0.72656239])
   

   #10 first values Test
   
   array([ 1.82212706,  1.98357668,  0.80547979, -0.19259114,  1.76072418,
           3.27855815,  2.12056804,  1.96099917,  2.38239663,  1.21005304])
   

2. This question is validated if the results match this output:

   r2 on the train set:  0.3552292936915783
   MAE on the train set:  0.5300159371615256
   MSE on the train set:  0.5210784446797679
   
   r2 on the test set:  0.30265471284464673
   MAE on the test set:  0.5454023699809112
   MSE on the test set:  0.5537420654727396
   

This result shows that the model has slightly better results on the train set than the test set. That's frequent since it is easier to get a better grade on an exam we studied than an exam that is different from what was prepared. However, the results are not good: r2 ~ 0.3. Fitting non linear models as the Random Forest on this data may improve the results. That's the goal of the exercise 5.