- ML Introduction
- ML Logistic Regression
- ML Linear Regression
- ML Data Clustering
- ML Categorical Data
- ML Decision Tree
- ML K-Means
- ML KNN
- ML Linear Graph
- ML Data Cleaning
- ML Data Distribution
- ML Standard Deviation
- ML Scatter Plot
- ML Polynomial Regression
- ML Confusion Matrix
- ML K Nearest Neighbor
- ML Training
- ML Data Distribution
- ML Classification Regression
- ML Random Forest
- ML Deployment
- ML Deep Learning
- ML Reinforcement Learning
- ML Dimensionality Reduction
- ML Neural Network
ML Classification Regression.
Regression and classification are both machine learning techniques, but they have different goals:
Regression
A supervised technique that predicts continuous values by plotting a line or curve that best fits the data. The goal is to predict the value of unknown data using known-value data.
Classification
A technique that analyzes the relationship between input variables and the likelihood that an instance belongs to a particular class.
Here are some examples of regression and classification techniques:
Linear regression
A supervised algorithm that uses a linear equation to model the relationship between a dependent variable and one or more independent variables. For example, linear regression can be used to predict house prices.
Logistic regression
A popular classification technique that uses the sigmoid function to transform linear regression output into a probability value between 0 and 1. It's commonly used for binary classification problems.
Polynomial regression
A type of regression that models non-linear datasets using a linear model. It's used to predict the model's accuracy and complexity in fields like social science, economics, biology, engineering, and physics.
Decision tree regression
A regression algorithm that builds a decision tree to predict the target value. A decision tree is a tree-like structure with nodes and branches, where each node represents a decision and each branch represents the outcome of that decision.