Machine Learning Random Forest with Python from Scratch - Classification versus Regression

The video tutorial covers the three main types of machine learning: classification, regression, and clustering. Classification is explained as a supervised learning method that predicts categorical outcomes, such as spam detection. Regression is described as predicting continuous values, like salary based on age. Various algorithms for both classification and regression are discussed, including logistic regression and random forest. The tutorial concludes with an introduction to clustering, a method used in unsupervised learning to classify data without labeled examples.

Which of the following is a type of unsupervised machine learning?

• Decision Trees

• Clustering

• Regression

• Classification

What is the primary goal of classification in machine learning?

• Optimizing decision boundaries

• Categorizing data into predefined classes

• Predicting continuous values

• Finding patterns in unlabeled data

In the context of classification, which of the following is an example of a categorical outcome?

• Predicting house prices

• Determining if an email is spam

• Estimating a person's salary

• Calculating the area of a plot

How does regression differ from classification in machine learning?

• Regression predicts categorical outcomes, while classification predicts continuous values.

• Regression and classification are both used for clustering data.

• Regression is used for unsupervised learning, while classification is for supervised learning.

• Regression predicts continuous values, while classification predicts categorical outcomes.

Which of the following is a common algorithm used for regression?

• Decision Trees

• Support Vector Machines

• Linear Regression

• Logistic Regression

Which algorithm can be used for both classification and regression tasks?

• K-Means Clustering

• Logistic Regression

• Naive Bayes

• Random Forest

What is the main characteristic of unsupervised machine learning?

• It uses labeled data for training.

• It categorizes data into predefined classes.

• It predicts continuous values.

• It finds patterns in unlabeled data.