Question

Why should we split our data into Training and Testing splits when building a model?

1) To increase the variance of the model

2) To decrease the bias of the model

3) To decrease the variance of the model

4) To increase the bias of the model

Answer 1

Splitting data into training and testing sets helps evaluate a model's generalization on unseen data and prevents overfitting. Sampling variability explains the differences in outcomes observed with different samples from the same population.

Step-by-step explanation:

When building a model, we split our data into Training and Testing splits to evaluate the performance of the model on unseen data. The main goal is neither to increase nor decrease the model's variance or bias prematurely, but to assess its ability to generalize. Specifically, it helps to prevent overfitting, ensuring that the model doesn't simply memorize the training data but can make accurate predictions on new, unseen data. Doing this is part of model validation, which aims to gauge the predictive performance and the reliability of the model before it's deployed in real-world applications.

Sampling variability is a fundamental concept in statistics that pertains to the differences observed when different samples are drawn from the same population. It is crucial to recognize that even well-chosen, representative samples can yield dissimilar data, but larger samples tend to approximate the population more accurately.