Now that we have a way to generate highly correlated data, we will use a lasso regression on it.
The aim of this part is to demonstrate the instability of the Lasso regression on this data.
For this purpose, we will use a loop to generate several datasets using **the same params** on our `generate_data` function.
We will demonstrate instability by counting the number of selected features each time, and registe which features are selected.
**Note :** the model auto-correct intercept by default `fit_intercept=True`.
The parameter `normalize` is deprecated, so we use a pipeline to normalize the data before the regression, as suggested in the deprecation message. We set the `with_mean` parameter to `False` because we just scale the data and do not center it, we leave this work to the Lasso.