• No products in the cart.

204.4.11 K-fold Cross Validation

Understanding and Practicing K-fold Cross validation
Link to the previous post : https://statinfer.com/204-4-10-cross-validation/

Ten-fold Cross – Validation

  • Divide the data into 10 parts(randomly)
  • Use 9 parts as training data(90%) and the tenth part as holdout data(10%)
  • We can repeat this process 10 times
  • Build 10 models, find average error on 10 holdout samples. This gives us an idea on testing error.

K-fold Cross Validation

  • A generalization of cross validation.
  • Divide the whole dataset into k equal parts
  • Use kth part of the data as the holdout sample, use remaining k-1 parts of the data as training data.
  • Repeat this K times, build K models. The average error on holdout sample gives us an idea on the testing error.
  • Which model to choose?
  • Choose the model with least error and least complexity.
  • Or the model with less than average error and simple (less parameters).
  • Finally use complete data and build a model with the chosen number of parameters.
  • Note: Its better to choose K between 5 to 10. Which gives 80% to 90% training data and rest 20% to 10% is holdout data.

Practice : K-fold Cross Validation

  • Build a tree model on the fiber bits data.
  • Try to build the best model by making all the possible adjustments to the parameters.
  • What is the accuracy of the above model?
  • Perform 10 -fold cross validation. What is the final accuracy?
  • Perform 20 -fold cross validation. What is the final accuracy?
  • What can be the expected accuracy on the unknown dataset?


In [34]:
##Defining the model parameters
tree_KF = tree.DecisionTreeClassifier(criterion='gini', 
In [35]:
#Importing kfold from cross_validation
from sklearn.cross_validation import KFold
In [36]:
#Simple K-Fold cross validation. 10 folds.
kfold = KFold(len(Fiber_df), n_folds=10)
In [37]:
## Checking the accuracy of model on 10-folds
from sklearn import cross_validation
score10 = cross_validation.cross_val_score(tree_KF,X, y,cv=kfold)
array([ 0.8358,  0.703 ,  0.6184,  0.8047,  0.8385,  0.7994,  0.7675,
        0.7507,  0.7913,  0.7206])
In [38]:
#Mean accuracy of 10-fold
In [39]:
#Simple K-Fold cross validation. 20 folds.
kfold = KFold(len(Fiber_df), n_folds=20)
In [40]:
#Accuracy score of 20-fold model
score20 = cross_validation.cross_val_score(tree_KF,X, y,cv=kfold)
array([ 0.9048,  0.781 ,  0.8288,  0.612 ,  0.283 ,  0.6676,  0.9226,
        0.7482,  0.907 ,  0.7866,  0.6784,  0.866 ,  0.8788,  0.911 ,
        0.925 ,  0.7318,  0.9724,  0.7502,  0.6954,  0.7456])
In [41]:
#Mean accuracy of 20-fold

With 10-fold kross validation we can expect Accuracy : 76.29%.

With 20-fold kross validation we can expect Accuracy : 77.98%.

The next post is about bootstrap cross validation.

Link to the next post : https://statinfer.com/204-4-12-bootstrap-cross-validation/

0 responses on "204.4.11 K-fold Cross Validation"

Leave a Message


Statinfer derived from Statistical inference is a company that focuses on the data science training and R&D.We offer training on Machine Learning, Deep Learning and Artificial Intelligence using tools like R, Python and TensorFlow

Contact Us

We Accept

Our Social Links

How to Become a Data Scientist

© 2020. All Rights Reserved.