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Building machine learning models is an important element of predictive modeling. However, without proper model validation, the confidence that the trained model will generalize well on the unseen data can never be high. Model validation helps in ensuring that the model performs well on new data, and helps in selecting the best model, the parameters, and the accuracy metrics.
In this guide, we will learn the basics and implementation of several model validation techniques, mentioned below:
#crossvalidation
Hold Out Validation
K-fold Cross-Validation.
Stratified K-fold Cross-Validation
Leave One Out Cross-Validation.
Repeated Random Test-Train Splits
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