Question

What is the purpose of splitting the dataset into testing and training sets?

1) Splitting the data allows us to test the trained model on "new" data that the model has not seen before.
2) The model can only be trained on a smaller section of data- the entire dataset would be too large.
3) The split allows us to rename the dataset subsets.
4) The split allows us to make sure we have fully imported the data.

Answer 1

Splitting the dataset into testing and training sets allows for model evaluation and avoids overfitting or underfitting. It also ensures a diverse range of samples for training while reserving data for evaluation.

Step-by-step explanation:

The purpose of splitting the dataset into testing and training sets is to evaluate the performance of a trained model on new, unseen data. By splitting the data, we can assess how well the model generalizes to real-world scenarios. This helps us understand if the model is overfitting or underfitting.

Additionally, splitting the dataset allows us to train the model on a subset of the data while reserving a separate portion for evaluation. This ensures that the model learns patterns from a diverse range of samples and is not biased towards specific data points.