Classification vs Regression

The video tutorial explains supervised learning, focusing on classification and regression problems. It describes how classification involves predicting categorical variables, while regression deals with continuous quantities. Examples include predicting student grades and smartphone prices, illustrating the application of classification and regression algorithms.

What is the primary difference between supervised learning and other types of machine learning?

• Supervised learning uses labeled data.

• Supervised learning does not require data.

• Supervised learning is only for image recognition.

• Supervised learning is faster than other types.

Which of the following is an example of a classification problem?

• Predicting the stock market trend.

• Predicting the price of a house.

• Predicting the final grade of a student.

• Predicting the temperature tomorrow.

What type of algorithm should be used to predict a student's final grade?

• Clustering algorithm

• Regression algorithm

• Dimensionality reduction algorithm

• Classification algorithm

Which scenario is an example of a regression problem?

• Predicting whether a student will pass or fail.

• Predicting the price of a smartphone.

• Classifying emails as spam or not spam.

• Determining the category of a news article.

How can a regression problem be transformed into a classification problem?

• By using a different dataset.

• By changing the output to discrete categories.

• By increasing the number of features.

• By using a different machine learning model.