How does Cross-Validation help in reducing overfitting?
- By adding noise to the data
- By allowing a more robust estimate of model performance
- By increasing the dataset size
- By regularizing the loss function
Cross-Validation reduces overfitting by allowing for a more robust estimate of the model's performance. By using different splits of the data, it ensures that the model's validation is not overly reliant on a specific subset, helping to detect if the model is overfitting to the training data.
Loading...
Related Quiz
- A telemedicine platform wants to develop a feature where patients can describe their symptoms in natural language, and the system provides potential diagnoses. This feature would heavily rely on which technology?
- The process of adding a penalty to the loss function to discourage complex models is called ________.
- What is the main function of the Gini Index in a Decision Tree?
- In the multi-armed bandit problem, the challenge is to balance between exploration of arms and ________ of the best-known arm.
- Suppose you're working on a dataset with both linear and nonlinear features predicting the target variable. What regression approach might you take?