Which of the following techniques can help prevent overfitting in supervised lea
Practice Questions
Q1
Which of the following techniques can help prevent overfitting in supervised learning?
Increasing the complexity of the model
Using more training data
Reducing the number of features
All of the above
Questions & Step-by-Step Solutions
Which of the following techniques can help prevent overfitting in supervised learning?
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: Recognize that using more training data can help. More data gives the model a better understanding of the overall patterns in the data.
Step 3: Consider other techniques to prevent overfitting, such as simplifying the model, using regularization, or employing techniques like cross-validation.
Step 4: Remember that the goal is to create a model that generalizes well to unseen data, not just performs well on the training set.
