Key algorithms in ML
Several algorithms in ML have pros and cons and suit different use cases.
In supervised learning, we have the following:
- Linear regression, which predicts a continuous output variable based on input features. It’s used in economics for forecasting and in healthcare for predicting disease progression.
- Logistic regression, which, despite its name, is an algorithm for binary classification problems and estimates the probability an instance belongs to a class. It’s used in credit scoring and medical testing.
- Decision tree, which learns simple decision rules inferred from data features. It’s useful in business decision-making and customer segmentation.
- Random forest, which uses multiple decision trees to prevent overfitting. This makes it an ensemble algorithm and is used in predicting disease risk, loan defaulters, and customer preferences.
- Support vector machine (SVM), which can model complex decision boundaries and...
