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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
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Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
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Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
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8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Different Types of Regression Models

Prashant
Prashant Last Updated : 05 Dec, 2024
7 min read

Regression analysis is a cornerstone of machine learning, crucial for modeling relationships between variables and making predictions. This article explores various types of linear regression and regression models, offering insights into their applications and distinctions. From the simplicity of linear regression to the complexities of ridge and lasso regression, understanding these models is essential for data-driven decision-making across diverse fields.

This article will cover about the regression models, variations of regression, and diverse forms of regression. Regression models is comprehension of the relationship between two or more factors. There are different forms of regression, each fulfilling distinct functions. Understanding the various forms of regression can assist in selecting the most suitable model for your data and inquiries.you will explore the different types of regression in machine learning, including various types of regression models.

Regression Models

Learning Outcomes

  • Understand the fundamental concepts of regression analysis and its importance in machine learning.
  • Differentiate between various types of linear regression models, including simple linear regression and multiple linear regression.
  • Identify the characteristics and applications of different regression models such as logistic regression, polynomial regression, and ridge regression.
  • Gain insights into advanced regression techniques like lasso regression, quantile regression, Bayesian linear regression and different types of regression.
  • Apply regression analysis to model and predict continuous outcomes based on relationships between variables.

This article was published as a part of the Data Science Blogathon.

Table of contents

What is a Regression Model/Analysis?

A regression model establishes a mathematical function that illustrates the connection between one or more independent variables and a dependent, response, or target variable.

You can use predictive modeling techniques such as regression models/analysis to determine the relationship between a dataset’s dependent (goal) and independent variables. Use these techniques widely when the dependent and independent variables are linked in a linear or non-linear fashion, and the target variable has a set of continuous values. Thus, regression analysis approaches help establish causal relationships between variables, modelling time series, and forecasting. Different types of regression, for example, is the best way to examine the relationship between sales and advertising expenditures for a corporation.

Also Read: What is Linear Regression?

What is the purpose of a Regression Models?

Use regression analysis for one of two purposes: predict the value of the dependent variable when you know the independent variables or predict the effect of an independent variable on the dependent variable.

Types of Regression Models

There are numerous regression analysis approaches available for making predictions. Various parameters, including the number of independent variables, the form of the regression line, and the type of dependent variable, determine the choice of technique.

Let us examine several of the most often utilized regression analysis techniques:

1. Linear Regression

The most extensively used modelling technique is linear regression, which assumes a linear connection between a dependent variable (Y) and an independent variable (X). It employs a regression line, also known as a best-fit line. The linear connection is defined as Y = c+m*X + e, where ‘c’ denotes the intercept, ‘m’ denotes the slope of the line, and ‘e’ is the error term.

The linear regression model can be simple (with only one dependent and one independent variable) or complex (with numerous dependent and independent variables) (with one dependent variable and more than one independent variable).

Linear Regression

2. Logistic Regression

When the dependent variable is discrete, the logistic regression technique is applicable. In other words, this technique is used to compute the probability of mutually exclusive occurrences such as pass/fail, true/false, 0/1, and so forth. Thus, the target variable can take on only one of two values, and a sigmoid curve represents its connection to the independent variable, and probability has a value between 0 and 1.

Logistic Regression | Regression Models

3. Polynomial Regression

Polynomial regression analysis represents a non-linear relationship between dependent and independent variables. This technique is a variant of the multiple linear regression model, but the best fit line is curved rather than straight.

Polynomial Regression

4. Ridge Regression

When data exhibits multicollinearity, that is, the ridge regression technique is applied when the independent variables are highly correlated. While least squares estimates are unbiased in multicollinearity, their variances are significant enough to cause the observed value to diverge from the actual value. Ridge regression reduces standard errors by biassing the regression estimates.

The lambda (λ) variable in the ridge regression equation resolves the multicollinearity problem.

Log Lambda, types of regression

5. Lasso Regression

As with ridge regression, the lasso (Least Absolute Shrinkage and Selection Operator) technique penalizes the absolute magnitude of the regression coefficient. Additionally, the lasso regression technique employs variable selection, which leads to the shrinkage of coefficient values to absolute zero.

Lasso Regression , types of regression models

6. Quantile Regression

The quantile regression approach is a subset of the linear regression technique. Statisticians and econometricians employ quantile regression when linear regression requirements are not met or when the data contains outliers.

Quantile Regression

7. Bayesian Linear Regression

Machine learning utilizes Bayesian linear regression, a form of regression analysis, to calculate the values of regression coefficients using Bayes’ theorem. Rather than determining the least-squares, this technique determines the features’ posterior distribution. As a result, the approach outperforms ordinary linear regression in terms of stability.

Bayesian Linear Regression

8. Principal Components Regression

Multicollinear regression data is often evaluated using the principle components regression approach. The significant components regression approach, like ridge regression, reduces standard errors by biassing the regression estimates. First, principal component analysis (PCA) modifies the training data, and then the resulting transformed samples train the regressors.

9. Partial Least Squares Regression

The partial least squares regression technique is a fast and efficient covariance-based regression analysis technique. It is advantageous for regression problems with many independent variables with a high probability of multicollinearity between the variables. The method reduces the number of variables to a manageable number of predictors, then uses them in regression.

10. Elastic Net Regression

Elastic net regression combines ridge and lasso regression techniques that are particularly useful when dealing with strongly correlated data. It regularizes regression models by utilizing the penalties associated with the ridge and lasso regression methods.

Elastic Net Regression, types of regression analysis

Also Read: 7 Regression Techniques You Should Know!

How to Create the Regression Model?

To create the Regression Model here is the following steps:

Define the Problem:

  • Figure out what you want to predict or explain (the outcome).
  • Identify the factors that might influence this outcome.

Gather Data:

  • Collect information related to your problem.
  • Make sure the data is clean and accurate.

Explore the Data:

  • Look for patterns and relationships between the factors and the outcome.
  • Use charts and graphs to visualize the data.

Choose a Model:

  • Select a type of model that best fits your problem (e.g., linear, logistic).

Train the Model:

  • Teach the model to recognize patterns in the data.
  • Use part of your data to train the model.

Test the Model:

  • Use a different part of your data to see how well the model works.
  • Check if it can accurately predict outcomes.

Make Predictions:

  • Use the trained model to predict new outcomes based on new data.

Interpret Results:

  • Understand what the model learned from the data.
  • Explain how the factors influence the outcome.

Conclusion

Regression analysis is a fundamental technique in machine learning and statistics, used to understand and predict relationships between variables. This article has explored various types of linear regression models, from simple to multiple regression, as well as advanced techniques like logistic regression, polynomial regression, and ridge regression. Each regression model serves different purposes, from predicting continuous outcomes to handling multicollinearity and non-linear relationships. By mastering these different types of regression techniques, data scientists can make informed decisions and predictions based on data, enhancing their ability to solve real-world problems across diverse fields.

I hope you will find this article informative on regression models and the different kinds of regression. Regression models enable us to examine the interactions between various variables. There exist various types of regression, each functioning slightly differently. Understanding the various forms of regression is crucial in selecting the appropriate one for the specific dataset and research query at hand.

Key Takeaways

  • Regression analysis is essential for predicting and understanding relationships between dependent and independent variables.
  • There are various regression models, including linear regression, logistic regression, polynomial regression, ridge regression, and lasso regression, each suited for different data scenarios.
  • Used for classification problems, it models the probability of binary outcomes.
  • Handles non-linear relationships by fitting a polynomial equation to the data.
  • Both techniques address multicollinearity by adding regularization terms to the regression equations, with lasso also performing variable selection.
  • Understanding different regression models enables better data-driven decisions in fields like economics, finance, healthcare, and social sciences.

Frequently Asked Questions

Q1. What are the types of regression models?

A. Types of regression models include linear regression, logistic regression, polynomial regression, ridge regression, and lasso regression.

Q2. What are the three regression models?

A. The three common regression models are linear regression, logistic regression, and polynomial regression.

Q3. Why is it called a regression model?

A. Regression models predict the relationship between dependent and independent variables by “regressing” the outcome variable based on the predictors.

Q4. What is the most common regression model?

A. The most common regression model is linear regression, widely used for its simplicity and effectiveness in predicting continuous outcomes.

Prashant
Prashant

Hello, my name is Prashant, and I'm currently pursuing my Bachelor of Technology (B.Tech) degree. I'm in my 3rd year of study, specializing in machine learning, and attending VIT University.

In addition to my academic pursuits, I enjoy traveling, blogging, and sports. I'm also a member of the sports club. I'm constantly looking for opportunities to learn and grow both inside and outside the classroom, and I'm excited about the possibilities that my B.Tech degree can offer me in terms of future career prospects.

Thank you for taking the time to get to know me, and I look forward to engaging with you further!

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Matilda Yankson

How well will I know the type of regression to use for my research question?

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This was really helpful thank you!

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