What is the purpose of using cross-validation in model evaluation?
Practice Questions
1 question
Q1
What is the purpose of using cross-validation in model evaluation?
To increase training time
To reduce overfitting
To improve model complexity
To increase dataset size
Cross-validation helps to assess how the results of a statistical analysis will generalize to an independent dataset, thus reducing overfitting.
Questions & Step-by-step Solutions
1 item
Q
Q: What is the purpose of using cross-validation in model evaluation?
Solution: Cross-validation helps to assess how the results of a statistical analysis will generalize to an independent dataset, thus reducing overfitting.
Steps: 5
Step 1: Understand that when we create a model, we want it to work well not just on the data we used to create it, but also on new, unseen data.
Step 2: Realize that if a model performs too well on the training data, it might be 'overfitting', meaning it learns the noise in the data instead of the actual patterns.
Step 3: Learn that cross-validation is a technique used to test how well the model will perform on new data by splitting the data into parts.
Step 4: In cross-validation, we train the model on some parts of the data and test it on other parts, repeating this process several times.
Step 5: By averaging the results from these tests, we get a better idea of how the model will perform on new data, helping to reduce overfitting.
