Types of Model Validation
Model validation is the step conducted post Model Training, wherein the effectiveness of the trained model is assessed using a testing dataset. This dataset may or may not overlap with the data used for model training.
Model validation can be broadly categorized into two main approaches based on how the data is used for testing:
1. In-Sample Validation
This approach involves the use of data from the same dataset that was employed to develop the model.
- Holdout method: The dataset is then divided into training set which is used to train the model and a hold out set which is used to test the performance of the model. This is a straightforward method, but it is prone to overfitting if the holdout sample is small.
2. Out-of-Sample Validation
This approach relies on entirely different data from the data used for training the model. This gives a more reliable prediction of how accurate the model will be in predicting new inputs.
- K-Fold Cross-validation: The data is divided into k number of folds. The model is trained on k-1 folds and tested on the fold that is left. This is repeated k times, each time using a different fold for testing. This offers a more extensive analysis than the holdout method.
- Leave-One-Out Cross-validation (LOOCV): This is a form of k-fold cross validation where k is equal to the number of instances. Only one piece of data is not used to train the model. This is repeated for each data point. Unfortunately, LOOCV is also time consuming when dealing with large datasets.
- Stratified K-Fold Cross-validation: k-fold cross-validation: in this type of cross-validation each fold has the same ratio of classes/categories as the overall dataset. This is useful especially where data in one class is very low compared to others.
What is Model Validation and Why is it Important?
Have you ever wondered if there is a way to check or evaluate the performance of a machine learning model you’ve trained? Is there a way or method to understand how the model responds to new or unseen data? The answer is yes, and it’s called Model Validation.
Before diving deeper into the article, let’s take a look at the article’s outline:
Table of Content
- What is Model Validation?
- Types of Model Validation
- 1. In-Sample Validation
- 2. Out-of-Sample Validation
- Importance of Model Validation
- Key Components of Model Validation
- 1. Data Validation
- 2. Conceptual Review
- 3. Testing
- Achieving Model Generalization
- Model Validation Techniques
- Benefits of Model Validation
- Conclusion
- Model Validation -FAQs