What is the difference between regression and classification?1 pointMachine learning models can be trained to do regression, but not classification.In regression, the input data is structured, but in classification the input data is unstructured.In regression, the correct answers have numerical value, but in classification the correct answers are categories.Machine learning models can be trained to do classification, but not regression.6.Question 6

What is the main difference between regression and classification tasks in supervised learning?Regression deals with discrete data, while classification deals with continuous dataRegression predicts a continuous output, while classification predicts discrete labelsThere is no difference; they are the sameRegression is used for unsupervised learning, while classification is used for supervised learning

Define classification and prediction. How are they different?

Regression vs. Clustering vs. Classification*

Question 2The "Regression" technique in Machine Learning is a group of algorithms that are used for:1 pointPrediction of class/category of a case; for example, a cell is benign or malignant, or a customer will churn or not.Finding items/events that often co-occur; for example grocery items that are usually bought together by a customer.Predicting a continuous value; for example predicting the price of a house based on its characteristics.

Which statement about Support Vector Machines is TRUE?1 pointSupport Vector Machine models can be used for regression but not for classification. Support Vector Machine models are non-linear. Support Vector Machine models can be used for classification but not for regression.Support Vector Machine models rarely overfit on training data.