Binary vs Multiclass Classification

The video tutorial explains the difference between binary and multiclass classification problems. Binary classification involves two classes, while multiclass classification involves more than two. The video provides examples, such as predicting fruit types or weather outlooks, to illustrate multiclass classification. It also discusses the importance of choosing the right classification algorithms, noting that some are designed for binary tasks but can be adapted for multiclass problems using heuristic methods like one-vs-rest and one-vs-one. The video emphasizes understanding these methods to effectively apply them in machine learning models.

What is an example of a multiclass classification problem?

• Determining if an email is spam or not

• Predicting the type of fruit based on measurements

• Classifying a transaction as fraudulent or not

• Identifying if a photo is of a cat or a dog

Which of the following algorithms is primarily designed for binary classification?

• Logistic Regression

• Random Forest

• Decision Tree

• K-Nearest Neighbors

Which method is used to adapt logistic regression for multiclass classification?

• Bagging

• Boosting

• One-vs-Rest

• Gradient Descent

What is the purpose of using heuristic methods in multiclass classification?

• To enhance the interpretability of the model

• To reduce the computational cost of training models

• To split a multiclass problem into multiple binary problems

• To improve the accuracy of binary classification

Which of the following is NOT a heuristic method for multiclass classification?

• Binary Splitting

• Random Sampling

• One-vs-Rest

• One-vs-One