clwen - 3 months ago 39

Python Question

How can one use

`cross_val_score`

`cross_val_score`

Tried the following two but doesn't work:

`scores = cross_validation.cross_val_score(svr, diabetes.data, diabetes.target, cv=5, scoring='mean_squared_error')`

and

`scores = cross_validation.cross_val_score(svr, diabetes.data, diabetes.target, cv=5, scoring=metrics.mean_squared_error)`

The first one generates a list of negative numbers while mean squared error should always be non-negative. The second one complains that:

`mean_squared_error() takes exactly 2 arguments (3 given)`

Answer

I dont have the reputation to comment but I want to provide this link for you and/or a passersby where the negative output of the MSE in scikit learn is discussed - https://github.com/scikit-learn/scikit-learn/issues/2439

In addition (to make this a real answer) your first option is correct in that not only is MSE the metric you want to use to compare models but R^2 cannot be calculated depending (I think) on the type of cross-val you are using.

If you choose MSE as a scorer, it outputs a list of errors which you can then take the mean of, like so:

```
# Doing linear regression with leave one out cross val
from sklearn import cross_validation, linear_model
import numpy as np
# Including this to remind you that it is necessary to use numpy arrays rather
# than lists otherwise you will get an error
X_digits = np.array(x)
Y_digits = np.array(y)
loo = cross_validation.LeaveOneOut(len(Y_digits))
regr = linear_model.LinearRegression()
scores = cross_validation.cross_val_score(regr, X_digits, Y_digits, scoring='mean_squared_error', cv=loo,)
# This will print the mean of the list of errors that were output and
# provide your metric for evaluation
print scores.mean()
```