How does overfitting affect the performance of a machine learning model? It improves generalization to unseen data It increases bias and decreases variance It decreases generalization to unseen data It has no effect on the model's performance
Question
How does overfitting affect the performance of a machine learning model?
It improves generalization to unseen data It increases bias and decreases variance It decreases generalization to unseen data It has no effect on the model's performance
Solution
Overfitting negatively affects the performance of a machine learning model. It happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means the model will have poor predictive performance, as it makes predictions based on noise, not on the underlying pattern. Therefore, the correct answer is "It decreases generalization to unseen data".
Similar Questions
How does overfitting affect the performance of a machine learning model? It improves generalization to unseen data It increases bias and decreases variance It decreases generalization to unseen data It has no effect on the model's performance
What is the consequence of a model having low bias and high variance? Overfitting Underfitting High generalization Low computational complexity
Which technique can help reduce overfitting in machine learning models? Increasing model complexity Decreasing the amount of training data Regularization Ignoring feature importance
Q.No 4. Which issue in machine learning refers to the model's inability to generalize wellto new, unseen data?a. Overfittingb. Underfittingc. Bias-variance tradeoffd. Model complexity
Overfitting occurs when a model:Question 7AnswerA.Has high bias and low varianceB.Has low bias and high varianceC. Performs well on unseen dataD. Fails to capture the underlying patterns in the data
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