# 📝 Exercise M7.03#

As with the classification metrics exercise, we will evaluate the regression metrics within a cross-validation framework to get familiar with the syntax.

We will use the Ames house prices dataset.

```
import pandas as pd
import numpy as np
ames_housing = pd.read_csv("../datasets/house_prices.csv")
data = ames_housing.drop(columns="SalePrice")
target = ames_housing["SalePrice"]
data = data.select_dtypes(np.number)
target /= 1000
```

Note

If you want a deeper overview regarding this dataset, you can refer to the Appendix - Datasets description section at the end of this MOOC.

The first step will be to create a linear regression model.

```
# Write your code here.
```

Then, use the `cross_val_score`

to estimate the generalization performance of
the model. Use a `KFold`

cross-validation with 10 folds. Make the use of the
\(R^2\) score explicit by assigning the parameter `scoring`

(even though it is
the default score).

```
# Write your code here.
```

Then, instead of using the \(R^2\) score, use the mean absolute error. You need
to refer to the documentation for the `scoring`

parameter.

```
# Write your code here.
```

Finally, use the `cross_validate`

function and compute multiple scores/errors
at once by passing a list of scorers to the `scoring`

parameter. You can
compute the \(R^2\) score and the mean absolute error for instance.

```
# Write your code here.
```