Cross Validation in Machine Learning: 4 Types of Cross Validation

Cross Validation in Machine Learning: 4 Types of Cross Validation

Model Development is a crucial step in a Data Science Project Life Cycle where we will try to train our data set with different types of Machine Learning models either of Supervised or Unsupervised Algorithms based on the Business Problem.

Introduction

Model Development is a crucial step in a Data Science Project Life Cycle where we will try to train our data set with different types of Machine Learning models either of Supervised or Unsupervised Algorithms based on the Business Problem.

As we are aware that we have a lot of models that can be used to solve a business problem we need to assure that whatever model we select at the end of this phase should be performing well on the unseen data. So, we cannot just go with the evaluation metrics in order to select our best performing model.

We need something more apart from the metric which can help us to decide on our final Machine Learning model which we can deploy to production.

The process of determining whether the mathematical results calculating relationships between variables are acceptable as descriptions of the data is known as Validation. Usually, an error estimation for the model is made after training the model on the train data set, better known as the evaluation of residuals.

In this process, we measure the Training Error by calculating the difference between predicted response and original response. But this metric cannot be trusted because it works well only with the training data. It’s possible that the model is Underfitting or Overfitting the data.

So, the problem with this evaluation technique or any other evaluation metric is that it does not give an indication of how well the model will perform to an unseen data set. The technique that helps to know this about our model is known as Cross-Validation.

In this article, we will get to know more about the different types of cross-validation techniques, pros, and cons of each technique. Let’s start with the definition of Cross-Validation.

Cross-Validation

Cross-Validation is a resampling technique that helps to make our model sure about its efficiency and accuracy on the unseen data. It is a method for evaluating Machine Learning models by training several other Machine learning models on subsets of the available input data set and evaluating them on the subset of the data set.

We have different types of Cross-Validation techniques but let’s see the basic functionality of Cross-Validation: The first step is to divide the cleaned data set into K partitions of equal size.

  1. Then we need to treat the Fold-1 as a test fold while the other K-1 as train folds and compute the score of the test-fold.
  2. We need to repeat step 2 for all folds taking another fold as a test while remaining as a train.
  3. Last step would be to take the average of scores of all the folds.

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