List of Supervised Machine Learning Algorithms Set 3

 

List of Supervised Machine Learning Algorithms

1.     List out Supervised Machine Learning Algorithms

supervised machine learning algorithms categorized into classification and regression, along with short real-life examples:

 

1.     Classification Algorithms

Logistic Regression:

Explanation: Predicts the probability of a binary outcome.

Real-Life Example: Classifying emails as "spam" or "not spam."

 

Decision Trees:

Explanation: Creates a tree structure where each node represents a feature and each branch represents a decision.

Real-Life Example: Diagnosing medical conditions based on symptoms.

 

Support Vector Machines (SVM):

Explanation: Finds the best boundary (hyperplane) that separates classes.

Real-Life Example: Classifying images of handwritten digits (e.g., recognizing digits in postal codes).

 

K-Nearest Neighbors (KNN):

Explanation: Classifies data points based on the classes of the nearest neighbors.

Real-Life Example: Recommending products to customers based on what similar customers bought.

 

Naive Bayes:

Explanation: Uses probabilities of feature values to make classifications.

Real-Life Example: Classifying text into categories like "sports," "politics," and "technology."

 

Random Forest:

Explanation: Combines multiple decision trees to improve classification accuracy.

Real-Life Example: Predicting customer churn by analyzing various customer behaviors and demographics.

 

2.             2. Regression Algorithms

Linear Regression:

Explanation: Predicts a continuous target variable based on the linear relationship between the target and input features.

Real-Life Example: Predicting house prices based on features like size, location, and number of bedrooms.

 

Decision Trees (for regression):

Explanation: Creates a tree structure where each node represents a feature and each branch represents a decision, aiming to predict a continuous target variable.

Real-Life Example: Estimating the value of a used car based on its age, mileage, and condition.

 

Support Vector Regression (SVR):

Explanation: Uses Support Vector Machines principles to predict continuous values.

Real-Life Example: Predicting stock prices based on historical data.

 

 

K-Nearest Neighbors (for regression):

Explanation: Predicts the value of a data point based on the average of its nearest neighbors' values.

Real-Life Example: Estimating the expected delivery time of a package based on similar past deliveries.

 

Random Forest (for regression):

Explanation: Combines multiple decision trees to predict continuous values.

Real-Life Example: Forecasting sales based on past sales data and various market factors.

 

Gradient Boosting Machines (GBM):

Explanation: Builds models sequentially, each new model correcting errors from the previous one, for predicting continuous target variables.

Real-Life Example: Predicting energy consumption in a building based on weather data and occupancy rates.

 

These algorithms are used widely in supervised machine learning to solve different classification and regression problems across various industries.