Model validation Methods
In principle, model validation is straightforward: after choosing a model and its hyperparameters, we can estimate how effective it is by applying it to some of the training data and comparing the prediction to the known value.
The Importance of Model Validation
Validating your machine learning model outcomes is all about making sure you’re getting the right data and that the data is accurate. Validation catches problems before they become big problems and is a critical step in the implementation of any machine learning model. Some added advantages of Model Validation are as follows.
Scalability and flexibility
Reduce the costs.
Enhance the model quality.
Discovering more errors
Prevents the model from overfitting and underfitting.
Model Validation Techniques
There are a number of different model validation techniques, choosing the right one will depend upon your data and what you’re trying to achieve with your machine learning model. These are the most common model validation techniques.
Train and Test Split or Holdout
The most basic type of validation technique is a train and test split. The point of a validation technique is to see how your machine learning model reacts to data it’s never seen before. All validation methods are based on the train and test split but will have slight variations.
With this primary validation method, you split your data into two groups: training data and testing data. You hold back your testing data and do not expose your machine learning model to it until it’s time to test the model. Most people use a 70/30 split for their data, with 70% of the data used to train the model.
K-Fold Cross-Validation
K-fold cross-validation is similar to the test split validation, except that you will split your data into more than two groups. In this validation method, “K” is used as a placeholder for the number of groups you’ll break your data into.
For example, you can split your data into 10 groups. One group is left out of the training data. Then you validate your machine learning model using the group that was left out of the training data. Then, you cross-validate. Each of the 9 groups used as training data is then also used to test the machine learning model. Each test and score can give you new information about what’s working and what’s not in your machine learning model.
Random Subsampling
Random subsampling functions in the same way to validate your model as does the train and test validation model. The key difference is that you’ll take a random subsample of your data, which will then form your test set. All of your other data that wasn’t selected in that random subsample is the training data.