What are the different classification methods in machine learning?

What are the different classification methods in machine learning?

There are several classification methods, including Decision Trees, Support Vector Machines, Naive Bayes, and Neural Networks. Each has its strengths and weaknesses.

Can you explain how Decision Trees work?

Sure! Decision Trees split the data into branches based on feature values, creating a tree-like model of decisions. It's intuitive and easy to interpret.

What about Support Vector Machines? How do they differ?

Support Vector Machines (SVM) find the hyperplane that best separates the classes in the feature space. They are effective in high-dimensional spaces.

Which method is the most accurate?

Accuracy can vary based on the dataset and problem. Generally, ensemble methods like Random Forests or Gradient Boosting tend to perform well.

Are there any methods that work better for imbalanced datasets?

Yes, methods like SMOTE for oversampling or using algorithms like Balanced Random Forest can help improve performance on imbalanced datasets.