It is the first time I am trying to use cross-validation and I am facing an error.
Firstly my dataset looks like this :
So, in order to avoid/reduce the overfitting of my model I am trying to use a k-fold cross validation.
from sklearn.model_selection import KFold
X,y = creation_X_y() #Function which is cleaning my data
kf = KFold(n_splits=5)
for train_index, test_index in kf.split(X):
print("Train:", train_index, "Validation:",test_index)
X_train = X[train_index]
X_test = X[test_index]
y_train, y_test = y[train_index], y[test_index]
However, I am facing the following error and I am not finding how I could solve it. I am understanding that it looks for these values in the columns but it should probably look in the index no ? May I use X.loc[train_index] for example ?
Thanks in advance for your time and your help !
Your assumption is correct: .iloc[index]
will work. Here is the code:
from sklearn.model_selection import KFold
X,y = creation_X_y() #Function which is cleaning my data
kf = KFold(n_splits=5)
for train_index, test_index in kf.split(X):
print("Train:", train_index, "Validation:",test_index)
X_train = X.iloc[train_index]
X_test = X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
Another way is to make creation_X_y()
return a numpy.array
.
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