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

The video tutorial covers the types of machine learning, focusing on supervised learning's classification and regression. Classification deals with categorical outputs, such as spam detection, while regression involves continuous outputs, like predicting salaries. Various algorithms for both methods are discussed, including logistic regression and random forest. The tutorial concludes with a preview of clustering, an unsupervised learning technique.

Which type of machine learning requires labeled data?

• Semi-supervised learning

• Unsupervised learning

• Reinforcement learning

• Supervised learning

What is the primary goal of classification in machine learning?

• Identifying clusters in data

• Predicting continuous values

• Optimizing decision-making processes

• Predicting categorical outcomes

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

• Estimating the age of a person

• Determining if an email is spam

• Predicting the price of a house

• Calculating the distance between two points

What type of output does a regression model produce?

• Categorical values

• Discrete categories

• Binary outcomes

• Continuous values

Which algorithm is commonly used for regression tasks?

• Support Vector Machines

• Linear Regression

• Decision Trees

• K-Means Clustering

Which algorithm can be used for both classification and regression?

• Principal Component Analysis

• Naive Bayes

• Random Forest

• K-Means

What is clustering primarily used for in unsupervised learning?

• Predicting future trends

• Optimizing algorithms

• Classifying labeled data

• Grouping similar data points