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10 Must-Know Machine Learning Algorithms for Data Scientists
Machine learning is the science of getting computers to act without being explicitly programmed.” — Andrew Ng
Machine learning algorithms are a crucial part of data science, allowing us to make predictions and understand complex data sets. In this guide, we will cover the top 10 machine learning algorithms that every data scientist should know.
1. K-Nearest Neighbors (KNN)
KNN is a simple but powerful classification algorithm that uses data point proximity to determine class membership. It works by identifying the K data points that are closest to the data point in question, and then assigning the data point to the class that is most represented among those K points.
Key features of KNN include:
- Easy to implement and understand
- Can be used for both classification and regression
- Flexible, as the number of nearest neighbors (K) can be adjusted
A real-world example of KNN in action is in credit scoring, where it can be used to predict the likelihood of a loan applicant defaulting on their loan.
2. Decision Trees
Decision trees are a type of supervised learning algorithm that can be used for both classification and regression tasks. They work by creating a tree-like structure that splits the data into smaller and smaller subsets based on certain rules or conditions. The final splits result in predictions or classifications for each data point.
Key features of decision trees include:
- Easy to understand and interpret
- Can handle both numerical and categorical data
- Can handle multiple input features
A real-world example of decision trees in action is in medical diagnosis, where they can be used to determine the most likely cause of a patient’s symptoms based on their medical history and test results.
