K Fold Cross-Validation & it's Implementation | Machine Learning

In this video, we are going to see how to implement Random forest with K-fold cross-validation. Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap and Aggregation, commonly known as bagging.

The K-fold cross-validation technique is a technique of resampling the data set in order to evaluate a machine learning model. In this technique, the parameter K refers to the number of different subsets that the given data set is to be split into. Further, K-1 subsets are used to train the model and the left-out subsets are used as a validation set.

To get the K-fold cross-validation we will split the dataset into three sets, Training, Testing, and Validation, with the challenge of the volume of the data.

1) Training- Here is the data being trained for the machine learning model to train the model and evaluate it on the validation set or test set

2) Testing- The machine learning Model will testing with an algorithm to Calculate prediction error

3) Validation- Generate overall prediction error by taking the average of prediction errors in every case.

Cross-Validation in Machine Learning: https://www.geeksforgeeks.org/cross-validation-machine-learning/