What is Cross Validation in Machine Learning and its Techniques?

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Machine Learning (ML) model development is not complete until, the model is validated to give the accurate prediction. The stability of model is important to rely on its decisions that should be correct and unbiased allowing to trust on the model.

Actually, there are various types of validation methods adopted depending whether the numerical results quantifying hypothesized relationships between variables, are acceptable as descriptions of the data. So, right here we will learn about what are the validation techniques used in ML model validation.

Holdout Method

Holdout is one of the most simple method to compute the overheads and its still suffers from the issues of high variance. This validation method helps to provide the final estimate of the machine learning model performance after it’s training and validation.

However, this method not take any overhead to compute and is better than other traditional validation. This validation process also suffer from high variance, as it not uncertain which data points will end up in the validation set and result could be different.

K-Fold Cross Validation

Enough data is not available there, and removing a part of it for validation poses a problem of underfitting into the model. If you reduce the training data, we risk losing the important patterns or trends in the data set, which resulting increases error induced by bias.

K-fold cross validation machine learning is the method that provides sufficient data for training the model and also leaves abundant data for validation. This validation method significantly reduces bias as we are using most of the data for fitting, and also significantly reduces variance as most of the data is also being used in validation set.

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cross validation machine learning

Stratified K-Fold Cross Validation

Stratified K-Fold cross validation is the process of rearranging the data so as to ensure that each fold is representative of the whole. Generally, it is better approach when a dealing with both bias and variance. A randomly selected fold might not properly represent the minor class, especially in the cases where there is huge class immense.

In various cases, there may be huge instability in the response variables. Hence, a slight variation in the K Fold cross validation technique is done, in a such way that each fold contains approximately the same percentage of samples of each target class as the complete set, or in case of prediction problems, the mean response value is approximately equal in all the folds.

Leave-P-Out Cross Validation

This is method, leaves p data as point of training data, i.e. if there are n data points in the original sample then, n-p samples are used to train the model and p points are used as the validation set. This is a repeated for all combinations in which original sample can be separated this way, and then the error is averaged for all trials, to give overall effectiveness.

Leave one out cross validation method is exhaustive in the sense that it needs to train and validate the model for all possible combinations, and for moderately large p, it can become computationally infeasible.

This method is generally preferred over the last one because it does not suffer from the intensive computation, as number of possible combinations is equal to number of data points in original sample or n. And a particular case of this method is when p = 1.

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However, cross validation is a very useful techniques for evaluating the effectiveness of the model, especially in case when you need to mitigate overfitting. And this method is also used to determine the hyper parameters of your model, in the sense that which parameters will give the lowest test error. Though, you can use any other types of validation technique as per your ease to validate the machine learning model without any bias.

Cogito is one of the companies, providing Machine learning services of model validation using the various types of validation techniques. It is doing this job in an unbiased manner evaluating the each model prediction level and correct the model with feeding the more accurate training data sets at very affordable cost. It is also providing the validation service for AI projects to make such models perfect and get the most accurate results.

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