RoundSqr Data Scientist Interview Questions and Answers

Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.

• Overfitting happens when a model is too complex and captures noise in the training data.

• It leads to poor performance on unseen data as the model fails to generalize well.

• Techniques to prevent overfitting include cross-validation, regularization, and early stopping.

• ...read more

Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.

• Overfitting happens when a model is too complex and captures noise in the training data.

• It leads to poor generalization and high accuracy on training data but low accuracy on new data.

• Techniques to prevent overfitting include cross-validation, regularization, and...read more