# ✅ Quiz#

Question

With a same dataset, feature importance might differs if:

• a) we use two different models

• b) we use two different train/test split with a same model

• c) we use a same model with a different set of hyper-parameters

• d) we use a same model with the same set of hyper-parameters but a different random_state

Question

In linear model, the feature importance:

• a) might be infer from the coefficients

• b) might be infer by importance_permutation

• c) need a regularization to infer the importance

• d) is a built-in attribute

Question

If two feature are the same (thus correlated)

• a) their feature importance will be the same

• b) their feature importance will be divided by 2

• c) only one will receive all the feature importance, the second one will be 0

• d) it depends

Question

The feature importance provided by the scikit-learn random forest:

• a) has bias for categorical feature

• b) has bias for continuous (high cardinality) feature

• c) is independent from the train/test split

• d) is independent from the hyper-parameters

Question

To evaluate the feature importance for a specific model, one could:

• a) drop a column and compare the score

• b) shuffle a column and compare the score

• c) put all column to 0 and compare the score

• d) change a column value to random number and compare the score