Understanding Machine Learning Concepts

What is the main output of a classification task?

• Training dataset size

• Predicted probability score

• Predicted class label

• Feature importance ranking

Which of the following is an example of regression?

• Predicting house prices based on various features.

• Calculating the average temperature over a week.

• Sorting a list of names alphabetically.

• Classifying emails as spam or not spam.

What does clustering aim to achieve?

• To group similar data points together.

• To create a linear regression model.

• To reduce the dimensionality of data.

• To analyze the performance of algorithms.

In classification, what type of label might you receive?

• Categorical or numerical labels

• Image labels

• Textual labels

• Boolean labels

Which use case is best suited for regression?

• Predicting continuous outcomes, such as house prices.

• Classifying images into categories.

• Forecasting the weather conditions for a week.

• Determining the sentiment of a text.

What is a common output of a clustering algorithm?

• A single data point

• Clusters of data points

• Randomly generated numbers

• A linear regression model

How does classification differ from regression?

• Classification predicts numerical values; regression predicts categories.

• Classification predicts categories; regression predicts continuous values.

• Classification is used for time series analysis; regression is for image recognition.

• Classification requires more data than regression for accuracy.

Can clustering be used to group customers?

• Yes, clustering can be used to group customers.

• Clustering is a method for predicting future sales.

• Clustering cannot be applied to customer data.

• Clustering is only used for image processing.