Random Forest Regression is an ensemble learning technique used for predicting continuous numeric values. It combines multiple decision trees to reduce overfitting and increase prediction accuracy. In Python, you can implement Random Forest Regression using Scikit-Learn. Here’s a step-by-step guide: Step 1: Import Libraries import numpy as np import matplotlib.pyplot …
Read More »Decision Tree Regression
Decision Tree Regression is a machine learning technique used for predicting continuous numeric values. It works by partitioning the data into smaller subsets based on the features and recursively splitting those subsets to create a tree-like structure. In Python, you can implement Decision Tree Regression using Scikit-Learn. Here’s a step-by-step …
Read More »Support Vector Regression
Support Vector Regression (SVR) is a regression technique that uses support vector machines to predict continuous numeric values. It’s particularly useful when dealing with non-linear and complex datasets. In Python, you can implement SVR using libraries such as Scikit-Learn. Below is a step-by-step guide on how to perform SVR in …
Read More »Polynomial Regression
Polynomial linear regression is a variation of linear regression where the relationship between the independent variable(s) and the dependent variable is modeled as an nth-degree polynomial. In Python, you can perform polynomial linear regression using the scikit-learn library. Here’s how you can do it: Import Necessary Libraries: import numpy as …
Read More »Multi Linear Regression
Multiple linear regression is a statistical method used to model the relationship between multiple independent variables (predictors) and a dependent variable (response) by fitting a linear equation to the observed data. In Python, the scikit-learn library provides a straightforward way to perform multiple linear regression. Here’s an overview of how …
Read More »Simple Linear Regression
Understanding Linear Regression in Machine Learning using Python Language Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value. Example: Let’s start with an example — suppose we have a dataset with information about the area of a house (in square feet) …
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