Regression techniques are used to model the relationship between a dependent variable and one or more independent variables. There are several types of regression techniques, each suited for different types of data and objectives:
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Linear Regression: This is the simplest form of regression, where the relationship between the dependent variable and independent variables is modeled as a linear equation. It assumes a straight-line relationship.
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Multiple Linear Regression: An extension of linear regression, this technique involves more than one independent variable to predict a dependent variable. It models the relationship as a linear equation with multiple predictors.
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Polynomial Regression: In cases where the relationship between variables is not linear, polynomial regression uses polynomial functions (e.g., quadratic, cubic) to model the data.
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Ridge Regression (L2 Regularization): A form of linear regression that adds a penalty term to the loss function to reduce the impact of collinearity and prevent overfitting by shrinking the coefficients of the regression model.
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Lasso Regression (L1 Regularization): Similar to ridge regression, but it uses L1 regularization to penalize the absolute value of coefficients. This can lead to some coefficients being reduced to zero, effectively performing feature selection.
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Elastic Net Regression: Combines the penalties of both ridge and lasso regression. It is useful when there are many variables or when the dataset has collinearity.
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Logistic Regression: Although it is used for classification tasks, logistic regression models the probability of the default class using a logistic function. It’s often used for binary outcomes.
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Stepwise Regression: A method for selecting the most significant variables by adding or removing predictors in a step-by-step manner, based on certain criteria (such as p-values or AIC).
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Quantile Regression: This technique models the conditional quantiles (e.g., the median or other percentiles) of the dependent variable, which is useful when the data has outliers or non-homogeneous variance.
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Robust Regression: Used when the data contains outliers, robust regression techniques (like Huber regression) reduce the impact of outliers by assigning less weight to them during model fitting.
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Support Vector Regression (SVR): A variant of support vector machines (SVM) that applies to regression problems. It aims to fit the best line within a margin of tolerance and handles non-linearity through kernel functions.
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Decision Tree Regression: This technique uses decision trees to model the relationship between variables by splitting the data into subsets based on the value of the predictors.
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Random Forest Regression: An ensemble method that uses multiple decision trees to improve prediction accuracy. It builds several decision trees and averages their predictions to reduce overfitting.
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Gradient Boosting Regression: An ensemble technique that builds multiple decision trees in a sequential manner, where each tree corrects the errors of the previous one, leading to a more accurate model.
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K-Nearest Neighbors Regression (KNN): A non-parametric method where the prediction for a data point is the average (or weighted average) of the values of its nearest neighbors.
Each of these techniques has specific use cases depending on the nature of the data, the complexity of the relationship, and the objective of the analysis.
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