## How do you define KFold?

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The general procedure is as follows:

- Shuffle the dataset randomly.
- Split the dataset into k groups.
- For each unique group: Take the group as a hold out or test data set. Take the remaining groups as a training data set.
- Summarize the skill of the model using the sample of model evaluation scores.

### What is cross-validation example?

For example, setting k = 2 results in 2-fold cross-validation. In 2-fold cross-validation, we randomly shuffle the dataset into two sets d0 and d1, so that both sets are equal size (this is usually implemented by shuffling the data array and then splitting it in two).

**How do you use KFold?**

The algorithm of the k-Fold technique:

- Pick a number of folds – k.
- Split the dataset into k equal (if possible) parts (they are called folds)
- Choose k – 1 folds as the training set.
- Train the model on the training set.
- Validate on the test set.
- Save the result of the validation.
- Repeat steps 3 – 6 k times.

**Why is KFold used?**

K-Folds technique is a popular and easy to understand, it generally results in a less biased model compare to other methods. Because it ensures that every observation from the original dataset has the chance of appearing in training and test set. This is one among the best approach if we have a limited input data.

## What does KFold return?

It will return the K different scores(accuracy percentage), which are based on kth test data set.

### What is Kfold in Python?

KFOLD is a model validation technique, where it’s not using your pre-trained model. Rather it just use the hyper-parameter and trained a new model with k-1 data set and test the same model on the kth set. K different models are just used for validation.

**What are the different types of cross-validation?**

There are various types of cross-validation. However, mentioned above are the 7 most common types – Holdout, K-fold, Stratified k-fold, Rolling, Monte Carlo, Leave-p-out, and Leave-one-out method. Although each one of these types has some drawbacks, they aim to test the accuracy of a model as much as possible.

**Why do we need k fold cross-validation?**

👉 k-Fold Cross-Validation: It ensures that the score of our model does not depend on the way we select our train and test subsets. In this approach, we divide the data set into k number of subsets and the holdout method is repeated k number of times.

## How does KFold split work?

KFold will provide train/test indices to split data in train and test sets. It will split dataset into k consecutive folds (without shuffling by default). Each fold is then used a validation set once while the k – 1 remaining folds form the training set (source).

### Does K-fold cross-validation prevent overfitting?

K-Fold cross-validation won’t reduce overfitting on its own, but using it will generally give you a better insight on your model, which eventually can help you avoid or reduce overfitting.

**What is 10k fold cross-validation?**

10-fold cross validation would perform the fitting procedure a total of ten times, with each fit being performed on a training set consisting of 90% of the total training set selected at random, with the remaining 10% used as a hold out set for validation. 9th Jan, 2015.