# Cross fold validation in relationship

### Improve Your Model Performance using Cross Validation (in Python / R)

Cross validation (CV) is one of the technique used to test the K-Fold is a popular and easy to understand, it generally results in a less biased. Alternately, the 5x2 fold cross-validation can be employed. It is generally better at detecting which algorithm is better (K-fold is generally better for determining. I'm curious if anybody is willing to discuss their validation approach - I tried random split (loose relationship @Peter Hurfold, does it mean for the stacking model using 10 fold cross validation might lead to more precise estimation than 5 fold?.

As we have seen above, less amount of data points can lead to a variance error while testing the effectiveness of the model We should iterate on the training and testing process multiple times.

### Cross-validation (statistics) - Wikipedia

Below are the steps for it: Always remember, a lower value of k is more biased, and hence undesirable. Precisely, LOOCV is equivalent to n-fold cross validation where n is the number of training examples. Stratified k-fold cross validation Stratification is the process of rearranging the data so as to ensure that each fold is a good representative of the whole. It is generally a better approach when dealing with both bias and variance. A randomly selected fold might not adequately represent the minor class, particularly in cases where there is a huge class imbalance.

Python code snippet for stratified k-fold cross validation: In such cases, one should use a simple k-fold cross validation with repetition.

In repeated cross-validation, the cross-validation procedure is repeated n times, yielding n random partitions of the original sample. The n results are again averaged or otherwise combined to produce a single estimation.

## Improve Your Model Performance using Cross Validation (in Python and R)

Python code for repeated k-fold cross validation: Adversarial Validation When dealing with real datasets, there are often cases where the test and train sets are very different. As a result, the internal cross-validation techniques might give scores that are not even in the ballpark of the test score. In such cases, adversarial validation offers an interesting solution.

The general idea is to check the degree of similarity between training and tests in terms of feature distribution. If It does not seem to be the case, we can suspect they are quite different.

Let us understand, how this can be accomplished in the below steps: Remove the target variable from the train set train. This method follows the below steps. The higher value of K leads to less biased model but large variance might lead to overfitwhere as the lower value of K is similar to the train-test split approach we saw before.

Then fit the model using the K — 1 K minus 1 folds and validate the model using the remaining Kth fold. Repeat this process until every K-fold serve as the test set.

Then take the average of your recorded scores.

**Machine Learning DataScience interview questions What is K-Fold Cross validation**

That will be the performance metric for the model. We can use the folds from K-Fold as an iterator and use it in a for loop to perform the training on a pandas dataframe. Below is the example. Holdout method[ edit ] In the holdout method, we randomly assign data points to two sets d0 and d1, usually called the training set and the test set, respectively. The size of each of the sets is arbitrary although typically the test set is smaller than the training set.

We then train build a model on d0 and test evaluate its performance on d1. In typical cross-validation, results of multiple runs of model-testing are averaged together; in contrast, the holdout method, in isolation, involves a single run. It should be used with caution because without such averaging of multiple runs, one may achieve highly misleading results. Similarly, indicators of the specific role played by various predictor variables e.

## Cross-validation (statistics)

While the holdout method can be framed as "the simplest kind of cross-validation", [10] many sources instead classify holdout as a type of simple validation, rather than a simple or degenerate form of cross-validation. For each such split, the model is fit to the training data, and predictive accuracy is assessed using the validation data. The results are then averaged over the splits.

The disadvantage of this method is that some observations may never be selected in the validation subsample, whereas others may be selected more than once. In other words, validation subsets may overlap. This method also exhibits Monte Carlo variation, meaning that the results will vary if the analysis is repeated with different random splits.

As the number of random splits approaches infinity, the result of repeated random sub-sampling validation tends towards that of leave-p-out cross-validation. In a stratified variant of this approach, the random samples are generated in such a way that the mean response value i. This is particularly useful if the responses are dichotomous with an unbalanced representation of the two response values in the data.

Measures of fit[ edit ] The goal of cross-validation is to estimate the expected level of fit of a model to a data set that is independent of the data that were used to train the model.

### Why and how to Cross Validate a Model? – Towards Data Science

It can be used to estimate any quantitative measure of fit that is appropriate for the data and model. For example, for binary classification problems, each case in the validation set is either predicted correctly or incorrectly.

In this situation the misclassification error rate can be used to summarize the fit, although other measures like positive predictive value could also be used. When the value being predicted is continuously distributed, the mean squared errorroot mean squared error or median absolute deviation could be used to summarize the errors. The reason that it is slightly biased is that the training set in cross-validation is slightly smaller than the actual data set e. In nearly all situations, the effect of this bias will be conservative in that the estimated fit will be slightly biased in the direction suggesting a poorer fit.