Which of the following techniques can help in reducing overfitting?
Practice Questions
Q1
Which of the following techniques can help in reducing overfitting?
Feature scaling
Regularization
Data augmentation
All of the above
Questions & Step-by-Step Solutions
Which of the following techniques can help in reducing overfitting?
Step 1: Understand what overfitting means. Overfitting happens when a model learns the training data too well, including noise and outliers, making it perform poorly on new data.
Step 2: Identify techniques that can help reduce overfitting. Common techniques include: regularization, cross-validation, pruning, dropout, and using more training data.
Step 3: Learn about regularization. This technique adds a penalty for larger coefficients in the model, which helps keep the model simpler.
Step 4: Understand cross-validation. This technique involves splitting the data into parts, training the model on some parts, and validating it on others to ensure it generalizes well.
Step 5: Explore pruning. In decision trees, pruning removes branches that have little importance, simplifying the model.
Step 6: Discover dropout. In neural networks, dropout randomly ignores some neurons during training, which helps prevent the model from becoming too reliant on any one feature.
Step 7: Recognize the importance of more training data. Providing more examples can help the model learn better patterns and reduce overfitting.
