📝 Exercise M7.03

📝 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
/tmp/ipykernel_5268/1042772600.py:1: DeprecationWarning: 
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
If this would cause problems for you,
please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466
        
  import pandas as pd

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.