I'm traying to do kfold validation:
X = df[['Smedications', 'Infections', 'lib' , 'north']].values
Y= df['Comorbidities'].values
kf = KFold(n_splits=10, shuffle=True)
list(kf.split(X))
splits = list(kf.split(X))
train_indices, test_indices = splits[0]
X_train = X[train_indices]
X_test = X[test_indices]
y_train = y[train_indices]
y_test = y[test_indices]
model = LogisticRegression()
model.fit(X_train, y_train)
print(model.score(X_test, y_test))
but I get this error message:
-----------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-90-752d1f80537e> in <module>()
12 X_train = X[train_indices]
13 X_test = X[test_indices]
---> 14 y_train = y[train_indices]
15 y_test = y[test_indices]
16
TypeError: only integer scalar arrays can be converted to a scalar index
Probably you either have arrays that are not numpy or indexes that are not of the int type. If it doesn't work, then show some rows with data X, Y.
X = df[['Smedications', 'Infections', 'lib' , 'north']].values
Y= df['Comorbidities'].values
kf = KFold(n_splits=10, shuffle=True)
list(kf.split(X))
splits = list(kf.split(X))
train_indices, test_indices = splits[0]
X_train = np.array(X)[train_indices.astype(int)]
X_test = np.array(X)[test_indices.astype(int)]
y_train = np.array(y)[train_indices.astype(int)]
y_test = np.array(y)[test_indices.astype(int)]
model = LogisticRegression()
model.fit(X_train, y_train)
print(model.score(X_test, y_test))
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