How to use k fold cross validation in sklearn
This recipe helps you use k fold cross validation in sklearn. The k fold cross validation procedure is a method for estimating the performance of a ML algorithm on a dataset. The k fold cross validation procedure divides a limited dataset into k non overlapping folds.
Recipe Objective - How to use k-fold cross-validation in sklearn?
K-Folds cross-validator.
The k-fold cross-validation procedure is a method for estimating the performance of an ML algorithm on a dataset. The k-fold cross-validation procedure divides a limited dataset into k non-overlapping folds. A total of k models are fit and evaluated on the k hold-out test sets and the mean performance is reported.
Each fold is then used once as validation while the k - 1 remaining folds form the training set.
Table of Contents
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Example:-
Step:1 Import Libraries:-
from sklearn.model_selection import KFold
import numpy as np
# create the range 1 to 25
rn = range(1,26)
Step:2 Creating Folds:-
# to demonstrate how the data are split, we will create 3 and 5 folds.
# it returns an location (index) of the train and test samples.
kf5 = KFold(n_splits=5, shuffle=False)
kf3 = KFold(n_splits=3, shuffle=False)
# the Kfold function retunrs the indices of the data. Our range goes from 1-25 so the index is 0-24
for train_index, test_index in kf3.split(rn):
print(train_index, test_index)
[ 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] [0 1 2 3 4 5 6 7 8]
[ 0 1 2 3 4 5 6 7 8 17 18 19 20 21 22 23 24] [ 9 10 11 12 13 14 15 16]
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16] [17 18 19 20 21 22 23 24]
KFold returns the index, if you want to see the real data we must use "np.take" in the NumPy array or ".iloc" in pandas.
# to get the values from our data, we use np.take() to access a value at particular index
for train_index, test_index in kf3.split(rn):
print(np.take(rn,train_index), np.take(rn,test_index))
[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25] [1 2 3 4 5 6 7 8 9] [ 1 2 3 4 5 6 7 8 9 18 19 20 21 22 23 24 25] [10 11 12 13 14 15 16 17] [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17] [18 19 20 21 22 23 24 25]![]()
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