Regression Algorithm in Machine Learning: Types, Use Cases and How to Choose (Updated August 2026)

Regression Algorithm in Machine Learning: Types, Use Cases and How to Choose (Updated August 2026) (Updated August 2026)

Regression is one of the two core tasks in supervised machine learning — and it is directly tied to some of the most valuable business predictions: what will this stock close at tomorrow, what is this property worth, what salary should this candidate expect. The NASSCOM-Deloitte report projects India needs 1.25 million AI professionals by 2027, and regression modelling is a skill tested in practically every data science technical interview. This guide, grounded in the ABC Trainings Proficient ML programme, covers what regression algorithms are, the key terminology you need, the types of regression, which algorithms to use and how to choose between them.

What Is a Regression Algorithm in Machine Learning?

A regression algorithm is a supervised machine learning technique used to predict a continuous numerical output based on one or more input variables. The word 'regression' comes from statistics — it describes modelling the relationship between a dependent variable (what you want to predict) and independent variables (the inputs that influence the prediction). Unlike classification which predicts a category, regression predicts a number. Is this stock worth buying? What will house prices be next quarter? How many units will this product sell? All of these are regression problems. In ML, regression algorithms learn the mathematical function that best maps your input features to your continuous output target — using historical data where both the inputs and correct outputs are known.

Key Regression Terminology: Response Variable, Predictor, Outliers and Overfitting

Before running a regression model, you need to know five terms. Response variable (also called dependent variable or target): the output you want to predict — house price, stock value, exam score. Predictor variable (independent variable): the inputs used to make the prediction — house size, distance from city, number of rooms. Outlier: a data point with an unusually high or low value compared to the rest of the dataset — outliers can distort regression model parameters significantly, especially in linear regression. Multicollinearity: high correlation between two or more predictor variables — this makes it hard to determine each predictor's individual effect and inflates coefficient uncertainty. Overfitting: when a model performs well on training data but poorly on new data — the model has memorised noise instead of learning the true pattern. Underfitting: when a model is too simple to capture the relationship in the data — poor performance on both training and test sets.

Algorithm	Best for	Outlier robust?	Interpretable?
Linear Regression	Linear relationships, baseline model	No	Yes
Polynomial Regression	Curved non-linear relationships	No	Moderate
SVR	Non-linear data, outlier tolerance	Yes	No
Decision Tree	Non-linear, explainable splits	Moderate	Yes
Random Forest	High accuracy, mixed data types	Yes	Partial

Three Types of Regression: Simple, Multiple and Non-Linear

There are three fundamental types of regression. Simple regression uses a single independent variable to predict the dependent variable — for example, predicting salary from years of experience. It assumes a linear relationship: as x increases by one unit, y changes by a fixed amount (the slope). Multiple regression uses two or more independent variables — for example, predicting house price from size, location, age and number of rooms simultaneously. It is the natural extension of simple regression and accounts for the combined influence of multiple predictors. Non-linear regression models a curved relationship between the independent and dependent variable — for example, predicting how a drug's effect varies non-linearly with dosage. Polynomial regression is the most common non-linear regression approach, adding squared and higher-order terms to capture curves while keeping the algorithm linear in its parameters.

Five Regression Algorithms Used in Real ML Projects

Five algorithms are commonly used for regression tasks. Linear regression is the simplest — it fits a straight line by minimising the sum of squared residuals (ordinary least squares). Fast, interpretable, but assumes linear relationships and normally distributed residuals. Polynomial regression adds polynomial feature terms (x², x³) to capture curves — useful when the relationship is clearly non-linear. Support Vector Regression (SVR) builds on the SVM framework — it tries to fit the best line within a margin of tolerance (epsilon tube), ignoring small errors. It is robust to outliers and handles non-linear data with the kernel trick. Decision tree regression partitions the feature space into rectangular regions and predicts the mean target value within each region — interpretable and non-parametric. Random forest regression builds many decision trees on bootstrap samples and averages their predictions — this ensemble approach reduces variance and produces more accurate, robust predictions than a single tree.

How to Choose the Right Regression Algorithm for Your Dataset

Choosing the right regression algorithm depends on five factors. Data linearity: if a scatter plot shows a clear straight-line pattern, start with linear regression. If the relationship curves, use polynomial regression or random forest. Number of features: simple regression for one feature; multiple regression or tree-based methods for many. Outlier sensitivity: linear regression is sensitive to outliers; random forest and SVR are more robust. Interpretability requirements: linear regression and decision trees are fully interpretable — each feature has a clear, explainable effect. Random forest is partially interpretable via feature importance; SVR is a black box. Dataset size: linear and polynomial regression scale to large datasets; SVR and individual decision trees are slower with very large data. Random forest is parallelisable and scales well. Start with linear regression as a baseline, then compare against a random forest — if random forest is significantly better, there are non-linear patterns worth capturing.

Real-World Applications of Regression in Industry

Regression algorithms are used across every industry where predictions drive decisions. In stock markets, regression models use historical prices, volume and economic indicators to forecast future price ranges — not precise point predictions, but ranges that inform buy/sell decisions. In real estate, regression models combine location, size, age, proximity to amenities and market conditions to estimate property values. In manufacturing at Pune facilities like Bajaj Auto and Bosch, regression models predict machine wear and maintenance cycles from sensor readings. In HR analytics, regression is used to predict employee attrition risk scores from tenure, salary gap and engagement survey data. In healthcare, regression models predict patient recovery time from treatment, age and comorbidity data. Every domain where you have historical numerical data and want to predict a future number is a potential regression application.

Regression Algorithm Training at ABC Trainings Pune

The ABC Trainings Proficient ML programme covers regression algorithms across dedicated sessions — starting with simple linear regression, moving through multiple regression and polynomial regression, then implementing SVR and random forest regression in Python with sklearn. Students build end-to-end projects: they collect or import a dataset, preprocess it, train multiple regression algorithms, compare RMSE and R² scores, and select the best model. This project-based approach is what produces interview-ready portfolios. Batches run at Wagholi and Hadapsar. ML programme fees start at ₹25,000; CMKPY-eligible students can claim up to ₹10,000 reimbursement. Call 7039169629 or WhatsApp 7774002496 for current batch dates.

Eligible students can apply for CMKPY (Chief Minister Yuva Karyaprasaran Yojana) skill training reimbursement of ₹6,000–₹10,000 toward approved ML and data science courses. Ask ABC Trainings whether the current ML batch is CMKPY-empanelled when you enquire.

FAQs

What is a regression algorithm in machine learning?

A regression algorithm is a supervised machine learning algorithm that predicts a continuous numerical output by learning the mathematical relationship between input features (independent variables) and the target (dependent variable). Examples include predicting house prices, stock values, employee salaries and product demand.

What is the difference between simple and multiple regression?

Simple regression uses one independent variable to predict the dependent variable — for example, salary from years of experience. Multiple regression uses two or more independent variables — for example, house price from size, location, age and amenities combined. Multiple regression is more realistic for real-world problems where outcomes are influenced by many factors simultaneously.

What is overfitting in regression and how do you fix it?

Overfitting in regression means the model has memorised the training data's noise rather than learning the true underlying pattern — it performs well on training data but poorly on new data. Common fixes: reduce model complexity (use fewer polynomial terms), add regularisation (Ridge or Lasso for linear models), use cross-validation to detect overfitting early, and collect more training data.

Which regression algorithm should a beginner start with?

Start with linear regression. It is the simplest regression algorithm, fully interpretable, fast to train and provides a meaningful baseline. Once you understand the concepts (slope, intercept, MSE, R²) with linear regression, polynomial regression, decision tree regression and random forest regression become much easier to understand as extensions. All are covered in the ABC Trainings ML programme.