Analysis Effect of K Values Used in K Fold Cross Validation for Enhancing Performance of Machine Learning Model with Decision Tree

In Data Science usual exercise is to reiteration throughout several models to observe a best working model. Creating portion of datasets to train and validate model for machine learning to improve performance the model. The splitting ratio of dataset is either 70:30 or 80:20. The problem with this technique is that only one large part is used to train and a small part is used to test ML model. Due to this approach sometimes, model get underfit or overfit. Objective of everyone is always find out the best fil model. CV is a technique which keep a portion of data from the entire dataset and used it for model testing (Validation set), and rest of data other than the part stored to train the ML model. In this paper we apply K fold cross-validation technique with Decision Tree Classifier. We have applied K fold CV by applying distinct K values with Decision Tree Classifier and checking accuracy, precision, recall and F1 value. From different research paper we found that it is difficult to decide the value of K. Our objective is to analyse and identified which value of K is most appropriate. By experimental analysis we found that the accuracy has been improved as compared to the traditional approach. By the observation we found that better for K is 10. BY the average accuracy, precision, recall and F1 value K fold gives better performance for K = 10. Real Life data set has been taken for experimental analysis.