Frequently Asked Questions

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2

2. What is the purpose of cross-validation?

The purpose of cross-validation is to test the ability of a machine learning model to predict new data.

3

3. What are the types of cross-validation?

Hold Out Method
Leave One Out Cross-Validation (LOOCV)
K-Fold Cross-Validation
Stratified Cross-Validation
Time Series Cross-Validation

D

Does cross-validation reduce Overfitting?

Yes. Cross-validation is used to avoid overfitting.

W

What is cross-validation?

Cross-Validation is a statistical method of evaluating and comparing learning algorithms by dividing data into two parts, one was used to learn or train our model and the other was used to validate our model.

Why we need it?

Because our model has trained itself on the given dataset, it already knows the data and has generalized over it very well. When we will try to predict new data, it gives us very bad accuracy, because it has not seen the new data before. It will fail to give us good accuracy and a generalized model.