In [6]:
from sklearn.model_selection import KFold
from sklearn.datasets import load_boston
import os, sys
import numpy as np
In [4]:
inner_cv = KFold(n_splits=5, shuffle=True, random_state=1)
outer_cv = KFold(n_splits=5, shuffle=True, random_state=1)
In [12]:
boston = load_boston()
X = boston.data
y = boston.target
print(X.shape)
In [21]:
train, test = list(inner_cv.split(X))[0]
train2, test2 = list(outer_cv.split(X))[0]
print(train, test)
print(train2, test2)
In [24]:
from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_score
diabetes = datasets.load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]
lasso = linear_model.Lasso()
print(cross_val_score(lasso, X, y, verbose=10))
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