By Jared Wilber & Brent Werness

In most supervised machine learning tasks, best practice recommends to split your data into three independent

Train,Test, and Validation Sets

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2022-05-12 16:30:09

By Jared Wilber & Brent Werness In most supervised machine learning tasks, best practice recommends to split your data into three independent sets: a training set , a testing set , and a validation set . To learn why, let's pretend that we have a dataset of two types of pets: Cats : Dogs : Each pet in our dataset has two features: weight and fluffiness . Our goal is to identify and evaluate suitable models for classifying a given pet as either a cat or a dog. We'll use train/test/validations splits to do this!

The first step in our classification task is to randomly split our pets into three independent sets: Training Set : The dataset that we feed our model to learn potential underlying patterns and relationships. Validation Set : The dataset that we use to understand our model's performance across different model types and hyperparameter choices. Test Set : The dataset that we use to approximate our model's unbiased accuracy in the wild.

The training set is the dataset that we employ to train our model. It is this dataset that our model uses to learn any underlying patterns or relationships that will enable making predictions later on. The training set should be as representative as possible of the population that we are trying to model. Additionally, we need to be careful and ensure that it is as unbiased as possible, as any bias at this stage may be propagated downstream during inference.