Cross-validation metrics in scikit-learn for each

Please, I just need to get the cross-validation statistics explicitly for each split of the (X_test, y_test) data.

So, to try to do so I did:

kf = KFold(n_splits=n_splits)

X_train_tmp = []
y_train_tmp = []
X_test_tmp = []
y_test_tmp = []
mae_train_cv_list = []
mae_test_cv_list = []

for train_index, test_index in kf.split(X_train):

    for i in range(len(train_index)):
        X_train_tmp.append(X_train[train_index[i]])
        y_train_tmp.append(y_train[train_index[i]])

    for i in range(len(test_index)):
        X_test_tmp.append(X_train[test_index[i]])
        y_test_tmp.append(y_train[test_index[i]])

    model.fit(X_train_tmp, y_train_tmp) # FIT the model = SVR, NN, etc.

    mae_train_cv_list.append( mean_absolute_error(y_train_tmp, model.predict(X_train_tmp)) # MAE of the train part of the KFold.

    mae_test_cv_list.append( mean_absolute_error(y_test_tmp, model.predict(X_test_tmp)) ) # MAE of the test part of the KFold.

    X_train_tmp = []
    y_train_tmp = []
    X_test_tmp = []
    y_test_tmp = []

Is it the proper way of getting the Mean Absolute Error (MAE) for each cross-validation split by using, for instance, KFold?

Thank you very much!

Maicon P. Lourenço

There are some issues with your approach.

To start with, you certainly don't have to append the data manually one by one in your training & validation lists (i.e. your 2 inner for loops); simple indexing will do the job.

Additionally, we normally never compute & report the error of the training CV folds - only the error on the validation folds.

Keeping these in mind, and switching the terminology to "validation" instead of "test", here is a simple reproducible example using the Boston data, which should be straighforward to adapt to your case:

from sklearn.model_selection import KFold
from sklearn.datasets import load_boston
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor

X, y = load_boston(return_X_y=True)
n_splits = 5
kf = KFold(n_splits=n_splits, shuffle=True)
model = DecisionTreeRegressor(criterion='mae')

cv_mae = []

for train_index, val_index in kf.split(X):
    model.fit(X[train_index], y[train_index])
    pred = model.predict(X[val_index])
    err = mean_absolute_error(y[val_index], pred)
    cv_mae.append(err)

after which, your cv_mae should be something like (details will differ due to the random nature of CV):

[3.5294117647058827,
 3.3039603960396042,
 3.5306930693069307,
 2.6910891089108913,
 3.0663366336633664]

Of course, all this explicit stuff is not really necessary; you could do the job much more simply with cross_cal_score. There is a small catch though:

from sklearn.model_selection import cross_val_score
cv_mae2 =cross_val_score(model, X, y, cv=n_splits, scoring="neg_mean_absolute_error")
cv_mae2
# result
array([-2.94019608, -3.71980198, -4.92673267, -4.5990099 , -4.22574257])

Apart from the negative sign which is not really an issue, you'll notice that the variance of the results looks significantly higher compared to our cv_mae above; and the reason is that we didn't shuffle our data. Unfortunately, cross_val_score does not provide a shuffling option, so we have to do this manually using shuffle. So our final code should be:

from sklearn.model_selection import cross_val_score
from sklearn.utils import shuffle
X_s, y_s =shuffle(X, y)
cv_mae3 =cross_val_score(model, X_s, y_s, cv=n_splits, scoring="neg_mean_absolute_error")
cv_mae3
# result:
array([-3.24117647, -3.57029703, -3.10891089, -3.45940594, -2.78316832])

which is of significantly less variance between the folds, and much closer to our initial cv_mae...