6.8 KiB
Exercise 0: Environment and libraries
The exercise is validated if all questions of the exercise are validated.
Activate the virtual environment. If you used conda run conda activate your_env.
Run python --version.
Does it print Python 3.x? x >= 8
Do import jupyter, import numpy, import pandas, import matplotlib and import sklearn run without any error?
Exercise 1: MSE Scikit-learn
Is the Mean Squared Error (MSE) calculated using the sklearn.metrics library?
Is the Mean Squared Error (MSE) correctly computed for the given y_true and y_pred values, and does the calculated MSE match the expected value?
Exercise 2: Accuracy Scikit-learn
Is the accuracy computed using the sklearn.metrics library?
Is the accuracy correctly calculated for the given y_true and y_pred values, and does the calculated accuracy match the expected value?
Exercise 3: Regression
The exercise is validated if all questions of the exercise are validated
For question 1, are the predictions on the train set and test set the following?
#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])
For question 2, Do the results match the following output (ensure that at least the first 3 decimal digits match)?
r2 on the train set: 0.6079874818809449
MAE on the train set: 0.5300159371615256
MSE on the train set: 0.5210784446797679
r2 on the test set: 0.5903435927516574
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.
Exercise 4: Classification
The exercise is validated if all questions of the exercise are validated
For question 1, are the predictions on the train set and test set the following?
# 10 first values Train
array([1, 0, 1, 1, 1, 0, 0, 1, 1, 0])
# 10 first values Test
array([1, 1, 0, 0, 0, 1, 1, 1, 0, 0])
For question 2, do the results match this output?
F1 on the train set: 0.9911504424778761
Accuracy on the train set: 0.989010989010989
Recall on the train set: 0.9929078014184397
Precision on the train set: 0.9893992932862191
ROC_AUC on the train set: 0.9990161111794368
F1 on the test set: 0.9801324503311258
Accuracy on the test set: 0.9736842105263158
Recall on the test set: 0.9866666666666667
Precision on the test set: 0.9736842105263158
ROC_AUC on the test set: 0.9863247863247864
For question 2, do the results match the confusion matrix on the test set? It should be:
array([[37, 2],
[ 1, 74]])
For question 3, Does the ROC AUC plot look like the plot below?
Having a 99% ROC AUC is not usual. The data set we used is easy to classify. On real data sets, always check if there's any leakage while having such a high ROC AUC score.
Exercise 5: Machine Learning models
For question 1, are the scores outputted close to the scores below? Some of the algorithms use random steps (random sampling used by the RandomForest). I used random_state = 43 for the Random Forest, the Decision Tree and the Gradient Boosting.
~~~
Linear Regression
TRAIN
r2 score: 0.6054131599242079
MAE: 0.5330920012614552
MSE: 0.5273648371379568
TEST
r2 score: 0.6128959462132963
MAE: 0.5196420310323714
MSE: 0.49761195027083804
~~~
SVM
TRAIN
r2 score: 0.749610858293664
MAE: 0.3835645163325988
MSE: 0.3346447867133917
TEST
r2 score: 0.7295080649899683
MAE: 0.38976805984267887
MSE: 0.3477101776543005
~~~
Decision Tree
TRAIN
r2 score: 1.0
MAE: 4.221907539810565e-17
MSE: 9.24499456646287e-32
TEST
r2 score: 0.6228217144931267
MAE: 0.4403051356589147
MSE: 0.4848526395290697
~~~
Random Forest
TRAIN
r2 score: 0.9741263135396302
MAE: 0.12000198560508221
MSE: 0.03458015083247723
TEST
r2 score: 0.8119778189909694
MAE: 0.3194169859011629
MSE: 0.24169750554364758
~~~
Gradient Boosting
TRAIN
r2 score: 0.8042086499063386
MAE: 0.35656543036682264
MSE: 0.26167490389525294
TEST
r2 score: 0.7895081234643192
MAE: 0.36455447680396397
MSE: 0.27058170064218096
It is important to notice that the Decision Tree overfits very easily. It learns easily the training data but is not able to extrapolate on the test set. This algorithm is not used a lot because of its overfitting ability.
However, Random Forest and Gradient Boosting propose a solid approach to correct the overfitting (in that case the parameters max_depth is set to None that is why the Random Forest overfits the data). These two algorithms are used intensively in Machine Learning Projects.
Exercise 6: Grid Search
The exercise is validated if all questions of the exercise are validated
For question 1, is the code that runs the gridsearch like the following? (the parameters may change):
parameters = {'n_estimators':[10, 50, 75],
'max_depth':[3,5,7],
'min_samples_leaf': [10,20,30]}
rf = RandomForestRegressor()
gridsearch = GridSearchCV(rf,
parameters,
cv = [(np.arange(18576), np.arange(18576,20640))],
n_jobs=-1)
gridsearch.fit(X, y)
For question 2, is the function as below?
def select_model_verbose(gs):
return gs.best_estimator_, gs.best_params_, gs.best_score_
In my case, the gridsearch parameters are not interesting. Even if I reduced the over-fitting of the Random Forest, the score on the test is lower than the score on the test returned by the Gradient Boosting in the previous exercise without optimal parameters search.
For question 3, is the code used the following?
model, best_params, best_score = select_model_verbose(gridsearch)
model.predict(new_point)