In the context of model selection, what does cross-validation help to achieve?
Practice Questions
Q1
In the context of model selection, what does cross-validation help to achieve?
Increase the training dataset size
Reduce overfitting and assess model performance
Select the best features
Optimize hyperparameters
Questions & Step-by-Step Solutions
In the context of model selection, what does cross-validation help to achieve?
Step 1: Understand that overfitting happens when a model learns the training data too well, including noise, and performs poorly on new data.
Step 2: Realize that cross-validation is a technique used to evaluate how well a model will perform on unseen data.
Step 3: Learn that in cross-validation, the data is split into several parts (or folds).
Step 4: Train the model on some parts of the data and test it on the remaining parts multiple times.
Step 5: Each time, a different part of the data is used for testing, which helps to ensure that the model is not just memorizing the training data.
Step 6: After all rounds of training and testing, the results are averaged to get a better estimate of the model's performance.
Step 7: Conclude that cross-validation helps to reduce overfitting and gives a more reliable assessment of how the model will perform in real-world scenarios.
