Supervised Learning: Regression and Classification for Exams

Supervised Learning: Regression and Classification - Numerical Applications

Q. In a classification problem, what does the term 'overfitting' refer to?

Solution

Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to new data.

Correct Answer: A — The model performs well on training data but poorly on unseen data

Q. What is the purpose of the training set in supervised learning?

Solution

The training set is used to train the model on labeled data, allowing it to learn the relationship between inputs and outputs.

Correct Answer: C — To train the model on labeled data

Q. What is the role of a validation set in supervised learning?

Solution

The validation set is used to tune hyperparameters and assess the model's performance during training, helping to prevent overfitting.

Correct Answer: C — To tune hyperparameters and prevent overfitting

Q. Which metric is best suited for evaluating a multi-class classification model?

Solution

F1 Score is a suitable metric for evaluating multi-class classification models as it considers both precision and recall.

Correct Answer: B — F1 Score

Q. Which of the following is a common application of regression analysis?

Solution

Regression analysis is commonly used to predict continuous outcomes, such as house prices based on various features.

Correct Answer: C — Predicting house prices

Q. Which of the following is a common application of supervised learning?

Solution

Spam detection is a common application of supervised learning, where the model is trained to classify emails as spam or not.

Correct Answer: B — Spam detection

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