# ✅ Quiz

```{admonition} 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
```

+++

```{admonition} 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
```

+++

```{admonition} 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
```

+++

```{admonition} 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
```

+++

```{admonition} 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
```