10 Regression Algorithms in Machine Learning: Which One Should You Actually Use in 2026?

Regression algorithms in machine learning are supervised learning methods that predict a continuous numerical value: a price, a demand figure, a temperature, a salary rather than a category. The ten used most often in industry are Linear, Polynomial, Ridge, Lasso, Elastic Net, Decision Tree, Random Forest, Gradient Boosting, Support Vector and K-Nearest Neighbours regression. For most real-world tabular datasets, start with Linear Regression as a baseline and Gradient Boosting as your accuracy benchmark.

Search for this topic and you will find the same list repeated across a dozen blogs: linear, ridge, lasso, decision tree, random forest, KNN, SVM. Useful as far as it goes. The problem is that almost none of those articles tell you the thing you actually need which algorithm to reach for when you are staring at a real dataset with 8,000 rows, 40 columns, three of them correlated and a deadline on Friday.

Worse, the most widely shared lists leave out the algorithm that wins most tabular regression problems in practice. A benchmark study presented at NeurIPS 2022 by Grinsztajn, Oyallon and Varoquaux tested modern deep learning methods against tree-based models including XGBoost and Random Forest across 45 datasets, and found tree-based models remained state of the art on medium-sized data of roughly 10,000 samples. Gradient boosting is not a footnote. On the kind of structured data most analysts and students work with, it is the model to beat and it is missing from nearly every popular guide to regression algorithms in machine learning.

This guide fixes that. You get all ten algorithms with the intuition, the honest limitations, an Indian use case for each, the scikit-learn class name so you can run it today, a master comparison table, a decision framework that maps your data to an algorithm, and the four evaluation metrics that decide whether your model is any good. Two specialist methods that show up in postgraduate syllabi are covered at the end.

Whether you are preparing for a semester examination, a technical interview or your first analytics role, the aim here is the same: to leave you able to look at a dataset and justify your choice among the types of regression in machine learning, rather than simply reciting their names.

What Is Regression in Machine Learning?

Regression is a supervised learning task in which a model learns the relationship between one or more input features and a continuous numerical target, then uses that learned relationship to predict the target for data it has never seen.

The word supervised matters. The model is trained on historical examples where the correct answer is already known, 5,000 past property sales with their actual sale prices, say and it adjusts itself until its predictions sit as close to those known answers as possible.

The simplest form, linear regression in machine learning, fits a straight line through the data:

y = β₀ + β₁x₁ + β₂x₂ + … + βₙxₙ + ε

Here y is the dependent variable, the thing you are predicting. Each x is an independent variable, or feature. Each β is a coefficient the model learns, β₀ is the intercept, and ε is the irreducible error. This is worth stating precisely because several widely circulated articles have the definition inverted, describing y as independent and x as dependent. It is the other way round: y depends on x.

The algorithm finds those coefficients using Ordinary Least Squares, which chooses the values that minimise the sum of the squared differences between predicted and actual values. Those differences are called residuals, and almost everything else in regression: regularisation, evaluation metrics, diagnostic plots is built on top of them.

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Regression vs Classification: The Distinction That Decides Everything

Before choosing among the types of regression in machine learning, be certain regression is the right family at all. The test is simple: look at your target column. If it holds numbers on a continuous scale, you need regression. If it holds labels, you need classification.

Table 1: Regression vs Classification at a Glance

One trap catches beginners constantly: logistic regression is a classification algorithm despite the name. It predicts the probability of a binary outcome, not a continuous quantity. It does not belong in a list of regression models in machine learning, and you will not find it below.

The 10 Regression Algorithms in Machine Learning, Explained

Each entry below follows the same structure: how it works, when to reach for it, when to avoid it, a use case grounded in the Indian market, and the scikit-learn class so you can test it immediately.

1. Linear Regression

Fits the single straight line or flat hyperplane in higher dimensions that minimises squared error across all training points. It is the oldest and most interpretable of the machine learning regression techniques, and every coefficient has a plain-English reading: hold everything else constant, and a one-unit rise in this feature moves the prediction by exactly β.

Simple linear regression in machine learning uses a single predictor; multiple linear regression uses several. The mechanics are identical, only the number of coefficients being estimated changes.

Use it when: The relationship is genuinely close to linear, features are few and not heavily correlated, and you need to explain the model to a business stakeholder or an examiner.

Avoid it when: The pattern curves, features are strongly correlated with one another, or outliers are present: squared error makes it hypersensitive to extreme values.

Indian use case: Forecasting daily electricity demand for a state discom from maximum temperature. The relationship is close to linear across the summer months, and the coefficient tells the planner exactly how many additional megawatts each degree costs.

scikit-learn: LinearRegression()

2. Polynomial Regression

Adds squared, cubed and higher-order versions of your existing features, then fits a linear model to that expanded feature set. The fitted curve bends, which is why it handles non-linear patterns but a detail almost every guide omits is that polynomial regression is still a linear model. It is linear in the coefficients, non-linear only in the features. That is why it trains as fast as ordinary linear regression.

Use it when: A scatter plot shows a clear, smooth curve, a rise then a fall, or accelerating growth and you have enough data to support the extra terms.

Avoid it when: You are tempted to push the degree past 3 or 4. High-degree polynomials swing wildly at the edges of the data range and overfit badly.

Indian use case: Modelling wheat yield against rainfall in Punjab. Yield climbs with rain up to a point, then falls as waterlogging sets in an inverted U that a straight line simply cannot represent.

scikit-learn: PolynomialFeatures() combined with LinearRegression()

3. Ridge Regression (L2 Regularisation)

Ridge keeps the linear regression objective but adds a penalty proportional to the sum of the squared coefficients. That penalty, controlled by a parameter usually written as alpha or lambda, discourages the model from assigning large weights to any single feature.

The practical effect is that Ridge tames multicollinearity. When two features carry nearly the same information, plain linear regression can hand one a huge positive coefficient and the other a huge negative one, producing a model that fits the training data but collapses on new data. Ridge shrinks both toward zero but never all the way to zero, so every feature stays in the model.

Use it when: Features are correlated, you have more features than is comfortable relative to your row count, and you believe most features contribute something.

Avoid it when: You specifically want the model to discard irrelevant features — Ridge will not remove any.

Indian use case: Property price prediction where carpet area, built-up area and super built-up area are all recorded and mutually correlated by construction.

scikit-learn: Ridge(alpha=1.0)

4. Lasso Regression (L1 Regularisation)

Lasso applies the same idea with one change: the penalty uses the absolute value of the coefficients rather than the square. That geometric difference has a large consequence, Lasso can drive coefficients to exactly zero, which removes those features from the model entirely. It performs prediction and feature selection in a single fit.

Two corrections are needed here, because both circulate widely. Lasso is not immune to overfitting: set the penalty too low and it behaves almost identically to unregularised linear regression. And Lasso is not a classification algorithm, it is a regularised linear regression method.

Use it when: You have many features and suspect only a handful genuinely matter, and you want an automatically sparse, readable model.

Avoid it when: Correlated features form a meaningful group. Lasso tends to keep one arbitrarily and discard the rest, which can be misleading.

Indian use case: A retail chain with 300 candidate signals — footfall, weather, festival calendar, competitor pricing, SKU attributes narrowing down to the dozen that actually drive store-level revenue.

scikit-learn: Lasso(alpha=0.1)

5. Elastic Net Regression

Elastic Net blends both penalties, with a mixing parameter deciding how much comes from L1 and how much from L2. It was designed specifically to fix Lasso’s weakness with correlated feature groups: where Lasso picks one and discards its correlates, Elastic Net tends to keep or drop the group together, which is usually what the underlying reality warrants.

This algorithm is absent from almost every popular list, which is a genuine omission, on wide datasets it routinely outperforms both Ridge and Lasso individually.

Use it when: You have many correlated features and want both feature selection and stability. It is the sensible default whenever you are unsure between Ridge and Lasso.

Avoid it when: You have few features and no correlation problem — the extra hyperparameter is not worth tuning.

Indian use case: Credit scoring models built on bureau data, where dozens of derived variables such as utilisation ratios and enquiry counts move together.

scikit-learn: ElasticNet(alpha=0.1, l1_ratio=0.5)

6. Decision Tree Regression

A decision tree splits the dataset repeatedly on feature thresholds, choosing at each step the split that most reduces variance within the resulting groups. To predict, it drops a new record down the tree and returns the average target value of the training records in whichever leaf it lands.

Trees need no feature scaling, handle non-linear relationships and interactions natively, and can be drawn on a whiteboard — which makes them the most explainable of the non-linear regression models in machine learning.

Use it when: You need a non-linear model that a non-technical audience can follow, or your features are on wildly different scales.

Avoid it when: You need stability. A single tree overfits readily and its structure can change substantially when a few training rows change. Always constrain depth or minimum samples per leaf.

Indian use case: A microfinance institution estimating a sanctionable loan amount, where the branch manager must be able to justify the figure to a borrower.

scikit-learn: DecisionTreeRegressor(max_depth=5)

7. Random Forest Regression

Random Forest trains hundreds of decision trees, each on a random bootstrap sample of the rows and considering only a random subset of features at each split, then averages their predictions. That deliberate injection of randomness decorrelates the trees, and averaging decorrelated errors cancels much of the variance that made a single tree unreliable.

It is the strongest algorithm here that works well with almost no tuning, and it reports feature importances for free.

Use it when: You want strong accuracy quickly, your data is tabular and moderately sized, and you have limited time for hyperparameter search.

Avoid it when: You must explain individual predictions precisely, or you need to extrapolate beyond the range seen in training — tree ensembles cannot.

Indian use case: Health insurance premium estimation from age, city tier, BMI, pre-existing conditions and claim history, where interactions between variables matter.

scikit-learn: RandomForestRegressor(n_estimators=300)

8. Gradient Boosting Regression (XGBoost, LightGBM, CatBoost)

This is the one the popular lists miss, and on structured data it is usually the most accurate option available.

Where Random Forest builds trees in parallel and averages them, gradient boosting builds them sequentially. The first tree makes a rough prediction. The second tree is trained specifically on the errors the first one made. The third corrects what remains, and so on for hundreds of rounds, each tree nudging the ensemble down the gradient of the loss function. The result is a model that keeps refining precisely where it is currently weakest.

The three production libraries — XGBoost, LightGBM and CatBoost — differ mainly in how they grow trees and handle categorical variables, but all implement the same core idea. This is the family that dominates tabular competitions on Kaggle and sits behind a great many production forecasting systems.

Use it when: Accuracy on tabular data is the priority and you can afford to tune learning rate, tree depth and number of estimators.

Avoid it when: Your data is very noisy and you cannot tune carefully — boosting will fit the noise. Always use early stopping on a validation set.

Indian use case: Demand forecasting for a quick-commerce platform predicting SKU-level order volume per dark store per hour, where festival spikes, weather and pincode demographics interact in complex ways.

scikit-learn: GradientBoostingRegressor() or HistGradientBoostingRegressor(); XGBRegressor() from the xgboost library

9. Support Vector Regression (SVR)

SVR inverts the usual objective. Instead of minimising every error, it defines a tolerance margin — an epsilon-insensitive tube — around the fitted function and ignores any point that falls inside it. Only the points outside the tube, the support vectors, influence the model at all. Combined with the kernel trick, which maps data into a higher-dimensional space without ever computing the coordinates explicitly, SVR captures complex non-linear relationships from relatively few examples.

Use it when: You have a small to medium dataset with many features, and the relationship is non-linear but smooth.

Avoid it when: You have more than roughly 50,000 rows — training cost grows steeply — or your data is very noisy. Feature scaling is mandatory.

Indian use case: Short-horizon commodity price forecasting on a mandi where only a few hundred clean historical observations exist.

scikit-learn: SVR(kernel=’rbf’, C=1.0, epsilon=0.1)

10. K-Nearest Neighbours Regression

KNN stores the training data and does no fitting at all. To predict, it finds the k most similar training records by distance and returns the average of their target values, optionally weighting nearer neighbours more heavily. It makes no assumption whatsoever about the shape of the relationship.

Because everything depends on distance, feature scaling is not optional — an unscaled column measured in rupees will overwhelm one measured in years.

Use it when: The dataset is small, features are few, and local similarity is genuinely meaningful — comparable-property valuation is the classic case.

Avoid it when: You have many features. In high dimensions all points become roughly equidistant and the notion of a nearest neighbour breaks down.

Indian use case: Valuing a flat in a Bengaluru locality by averaging the per-square-foot rates of the most comparable recent sales nearby — precisely how a human valuer already works.

scikit-learn: KNeighborsRegressor(n_neighbors=5, weights=’distance’)

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Two More You Will Meet in a Postgraduate Syllabus

Neural Network Regression

A feed-forward network becomes a regressor when the output layer uses a single neuron with a linear activation instead of a softmax. Hidden layers with non-linear activations let it approximate almost any function, which is genuinely powerful — but on ordinary tabular data it usually needs far more rows and far more tuning than gradient boosting to reach the same accuracy. Its real advantage appears when inputs include images, text or sequences alongside numbers.

Gaussian Process Regression

GPR does something none of the others do: it returns a full probability distribution over predictions rather than a single number, so every prediction arrives with a calibrated confidence interval. That makes it the method of choice when quantified uncertainty matters more than raw accuracy — scientific experiments, sensor calibration, Bayesian optimisation of hyperparameters. The cost is computational, and it becomes impractical beyond a few thousand rows.

Master Comparison: All 10 Regression Algorithms in Machine Learning

This is the table to keep open while you work. It compares every algorithm above on the five properties that actually determine your choice.

Table 2: Comparing the 10 Regression Algorithms Side by Side

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How to Choose: A Decision Framework That Takes Sixty Seconds

Most guides end with vague advice about considering your data. Here is something more usable. Read down the left column until you find the row that describes your situation, then start with the algorithm on the right.

Table 3: Matching Your Data Situation to the Right Algorithm

One principle underpins the whole table: always fit a plain linear regression first, even when you are certain it will lose. It costs seconds and gives you a baseline. If your carefully tuned gradient boosting model beats it by two percent, that is a signal worth acting on — the extra complexity is buying you almost nothing, and the simpler model will be easier to deploy, explain and maintain.

Treat machine learning regression techniques as a toolkit rather than a ranking. The competent practitioner is not the one who knows the most advanced algorithm, but the one who reliably picks the simplest model that meets the accuracy the problem actually requires.

How to Tell Whether Your Model Is Any Good: The Four Metrics

This is the section competitors skip entirely, and it is the one that separates a student who has read about machine learning regression techniques from one who can use them. Choosing an algorithm is half the work; knowing whether its output is trustworthy is the other half.

Table 4: Regression Evaluation Metrics and When to Use Each

Report at least two. RMSE alongside MAE tells you something a single number cannot: if RMSE sits far above MAE, your model is making a small number of very large mistakes, and finding those cases usually teaches you more than any amount of hyperparameter tuning. And always compute these on a held-out test set the model has never seen, never on the data it trained on.

Model evaluation is the single most tested skill in analytics interviews, and it appears in almost every job description for a career in AI and machine learning in India. Candidates who can name ten algorithms but cannot explain why they chose RMSE over MAPE tend not to progress past the first round.

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The Four Assumptions Behind Linear Regression

Linear regression in machine learning — along with its regularised cousins Ridge, Lasso and Elastic Net — rests on four assumptions. Examiners ask about these constantly, and violating them silently is one of the most common reasons a model that looks fine on paper fails in production. The mnemonic is LINE.

• Linearity — the relationship between each predictor and the target is genuinely linear. Check with a plot of residuals against fitted values; any visible curve means this is violated.
• Independence — residuals are independent of one another. Time-series data frequently violates this, because today’s error correlates with yesterday’s. The Durbin-Watson statistic tests for it.
• Normality — residuals follow a normal distribution. This matters for confidence intervals and significance tests rather than for the point predictions themselves. Check with a Q-Q plot.
• Equal variance, or homoscedasticity — residual spread stays constant across the range of fitted values. A funnel shape in the residual plot means variance is growing, and a log transform of the target often fixes it.

A fifth condition, no perfect multicollinearity, is why Ridge and Elastic Net exist at all. Check it with the Variance Inflation Factor; a VIF above 10 for any feature is a warning. Tree-based regression models in machine learning are free of all five constraints, which is a substantial part of why they are so widely used.

Six Mistakes That Sink Regression Projects

1. Evaluating on training data. A model that scores an R² of 0.98 on the data it learned from has told you nothing. Split your data, or use k-fold cross-validation.
2. Skipping feature scaling for distance-based and kernel-based methods. KNN and SVR require it. Tree-based methods do not care.
3. Reaching for a neural network on 800 rows. Complex models need data volume to justify themselves; on small tabular datasets a regularised linear model or a tuned boosting model will almost always win.
4. Ignoring multicollinearity and then interpreting coefficients. When features are correlated, individual coefficients become unstable and the story they tell can be wrong even when predictions look acceptable.
5. Letting data leak from the future into training. Scaling or imputing across the full dataset before splitting quietly leaks test information into training and inflates every score.
6. Optimising one metric and reporting it alone. A model tuned to minimise RMSE may be badly biased in the percentage terms that a business actually cares about.

Where You Actually Learn to Build These Models

Reading about regression algorithms in machine learning gets you to the point of recognising the names. Building models that survive contact with messy data requires structured practice on the mathematics underneath, the Python tooling around it, and a project you have taken from raw data to a working prediction.

Jaipur National University’s Centre for Distance and Online Education runs several UGC-DEB entitled programmes that cover exactly this ground. JNU holds recognition under Section 2(f) of the UGC Act, UGC-DEB entitlement for its online and distance programmes, NAAC A+ accreditation and membership of the Association of Indian Universities — so the qualification carries the same standing as an equivalent on-campus degree.

Online Diploma in Artificial Intelligence & Machine Learning — one year, 36 credits

The most direct route. The first semester builds the foundation with Mathematics for AI & ML, Introduction to Python with a dedicated Python Lab, Introduction to Artificial Intelligence & Machine Learning, and an Artificial Intelligence Lab using Python. The second semester moves into Machine Learning & Pattern Recognition, Data Handling & Pre-processing with its own lab, Data Visualization, Deep Learning & Neural Networks with TensorFlow, and Natural Language Processing & Generative AI, closing with a four-credit Capstone Project. Eligibility is 10+2 with 40% marks.

Online Diploma in Data Science — one year, 34 credits

Better suited if your interest is the analytics side. It opens with Mathematics & Statistics for Data Science, Introduction to Python, Database Management Systems and Data Visualization, each with practical lab components. Semester two covers R Programming for Data Science, Big Data Analytics, Data Analysis Using Python and Data Analytics, again finishing with a Capstone Project. Also open to 10+2 candidates with 40% marks.

Online MCA — two years, ₹1,06,400

The full postgraduate route for those who want the degree alongside the skills. Semester II covers Computer Based Optimization Techniques; Semester III includes Introduction to Artificial Intelligence and Machine Learning as a core paper, an Artificial Intelligence Lab using Python, and Big Data Analytics as a discipline elective. The fee works out to ₹26,600 per semester. Applicants need a three-year bachelor’s degree with 40% marks and Mathematics as a subject; those without it complete a bridge programme.

Online BCA — three years, ₹1,04,160

The undergraduate entry point. Semester V covers Data Warehousing and Data Mining, and Semester VI covers R Programming and Python Programming with a dedicated lab and a final project — the exact toolchain used to implement every algorithm in this article. Open to 10+2 candidates with 40% marks, at ₹17,360 per semester.

Online M.Sc Mathematics — two years, ₹67,200

The route for anyone who wants the mathematics rather than the tooling. Linear Algebra in Semester III is the language in which every regression coefficient is actually computed; Numerical and Statistical Techniques in Semester II, with its accompanying lab, and Mathematical Statistics in Semester IV cover the estimation theory that regularisation and significance testing are built on. At ₹16,800 per semester it is the most economical of the five, and it is open to graduates of any stream.

Choosing between them usually comes down to how much time you have and what you want the credential to do. A one-year diploma in data science adds a job-ready skill quickly; a postgraduate degree carries more weight for long-term progression, and the trade-offs between MSc Computer Science and MCA are worth understanding before you commit. Students coming from an undergraduate computing background often ask what becoming a software developer after BCA actually involves — regression modelling is one of the routes into it.

All programmes run on JNU’s web and mobile LMS with self-paced study, which is what makes them workable alongside a job. Applications go through the JNU Online admission portal.

Conclusion

The ten regression algorithms in machine learning covered here are not ten interchangeable options. They form a rough ladder of complexity, and the discipline lies in climbing it only as far as your data justifies.

Begin with linear regression to establish a baseline you can beat. Add Ridge, Lasso or Elastic Net when correlation or feature count becomes a problem. Move to Random Forest when the relationship is clearly non-linear and you want results fast. Reach for gradient boosting when accuracy on tabular data is the objective and you have time to tune. Keep SVR, KNN and Gaussian Process Regression for the specific situations where each is genuinely the right instrument — small data, local similarity, quantified uncertainty.

And whichever you choose, evaluate it on data it has never seen, report more than one metric, and check your assumptions before you trust a coefficient. That habit will matter more to your work than knowing any single algorithm well.

Frequently Asked Questions

What are regression algorithms in machine learning?

They are supervised learning methods that predict a continuous numerical value from input features — a price, a demand quantity, a temperature — by learning the relationship between those features and a known target in historical data. Classification algorithms, by contrast, predict categories.

Which regression algorithm is the most accurate?

There is no universally best algorithm, but on structured tabular data gradient boosting implementations such as XGBoost, LightGBM and CatBoost most often produce the highest accuracy. Benchmark research presented at NeurIPS 2022 found tree-based models remained state of the art against deep learning on medium-sized tabular datasets of around 10,000 samples.

What is the difference between Ridge and Lasso regression?

Ridge penalises the sum of squared coefficients and shrinks them toward zero without ever reaching it, so all features are retained. Lasso penalises the sum of absolute coefficients and can set some to exactly zero, removing those features entirely and performing automatic feature selection.

Is logistic regression a regression algorithm?

No, despite the name. Logistic regression predicts the probability of a category rather than a continuous value, which makes it a classification algorithm. It is included in regression lists frequently and incorrectly.

How many types of regression in machine learning are there?

There is no fixed number, since variants can be grouped in several ways. Ten algorithms cover the overwhelming majority of practical work: Linear, Polynomial, Ridge, Lasso, Elastic Net, Decision Tree, Random Forest, Gradient Boosting, Support Vector and K-Nearest Neighbours regression. Neural network and Gaussian process regression add two more for specialist cases.

Which algorithm handles overfitting best?

Methods with built-in regularisation resist overfitting most reliably — Ridge, Lasso and Elastic Net among linear models, and Random Forest among ensembles. Gradient boosting can be very strong but will overfit if left untuned, so it needs early stopping and a validation set.

Do I need to scale features before running a regression model?

It depends on the algorithm. K-Nearest Neighbours and Support Vector Regression require scaling because both rely on distance. Ridge, Lasso and Elastic Net need it for the penalty to apply fairly across features. Decision trees, Random Forest and gradient boosting do not require scaling at all.

Is R² alone enough to judge a regression model?

No. R² rises automatically whenever you add a feature, even a useless one, so it can flatter a bloated model. Use Adjusted R² when comparing models with different feature counts, and always pair it with an error metric in the target’s own units such as RMSE or MAE.

What is the best way to learn these algorithms properly?

Combine three things: the mathematics of linear algebra and statistics, hands-on implementation in Python or R, and at least one end-to-end project on messy real data. JNU Online’s one-year Diploma in AI & ML and Diploma in Data Science both structure exactly that sequence and end with a capstone project, while the Online MCA and BCA embed the same material inside a full degree.

Are online degrees in AI and machine learning valid in India?

Yes, provided the university holds UGC-DEB entitlement for the programme. Under UGC regulations, online and distance degrees from entitled institutions are treated as equivalent to on-campus degrees for employment and further study. Before applying anywhere, it is worth verifying the institution against the list of UGC-approved online degree courses in India. Jaipur National University is UGC-DEB entitled, holds NAAC A+ accreditation and is a member of the Association of Indian Universities.

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