{"id":830,"date":"2026-08-10T11:49:30","date_gmt":"2026-08-10T11:49:30","guid":{"rendered":"https:\/\/online.jnujaipur.ac.in\/blogs\/?p=830"},"modified":"2026-08-14T07:59:56","modified_gmt":"2026-08-14T07:59:56","slug":"regression-algorithms-in-machine-learning","status":"publish","type":"post","link":"https:\/\/online.jnujaipur.ac.in\/blogs\/regression-algorithms-in-machine-learning\/","title":{"rendered":"10 Regression Algorithms in Machine Learning: Which One Should You Actually Use in 2026?"},"content":{"rendered":"

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.<\/span><\/p>\n

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.<\/span><\/p>\n

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.<\/span><\/p>\n

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.<\/span><\/p>\n

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.<\/span><\/p>\n

What Is Regression in Machine Learning?<\/b><\/h2>\n

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.<\/span><\/p>\n

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.<\/span><\/p>\n

The simplest form, linear regression in machine learning, fits a straight line through the data:<\/span><\/p>\n

y = \u03b2\u2080 + \u03b2\u2081x\u2081 + \u03b2\u2082x\u2082 + \u2026 + \u03b2\u2099x\u2099 + \u03b5<\/i><\/b><\/p>\n

Here y is the dependent variable, the thing you are predicting. Each x is an independent variable, or feature. Each \u03b2 is a coefficient the model learns, \u03b2\u2080 is the intercept, and \u03b5 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.<\/span><\/p>\n

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.<\/span><\/p>\n

Know More: Types of Indexes in SQL Server: All 12 Types Explained with Syntax (2026)<\/a><\/strong><\/p>\n

Regression vs Classification: The Distinction That Decides Everything<\/b><\/h2>\n

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.<\/span><\/p>\n

Table 1: Regression vs Classification at a Glance<\/b><\/p>\n\n\n\n\n\n\n\n\n\n\n
Aspect<\/th>\nRegression<\/th>\nClassification<\/th>\n<\/tr>\n<\/thead>\n
Target variable<\/td>\nContinuous number (\u20b9, kg, \u00b0C, units)<\/td>\nDiscrete label or category<\/td>\n<\/tr>\n
Example question<\/td>\nWhat will this flat sell for?<\/td>\nWill this loan default \u2014 yes or no?<\/td>\n<\/tr>\n
Typical output<\/td>\n\u20b968,42,000<\/td>\nDefault \/ No default<\/td>\n<\/tr>\n
Core metrics<\/td>\nRMSE, MAE, MAPE, R2<\/sup><\/td>\nAccuracy, Precision, Recall, F1, AUC<\/td>\n<\/tr>\n
Loss function<\/td>\nSquared error, absolute error, Huber<\/td>\nCross-entropy, hinge loss<\/td>\n<\/tr>\n
Shared algorithms<\/td>\nDecision Tree, Random Forest, Gradient Boosting, SVM and neural networks work
\nfor both \u2014 only the output layer and loss function change<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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.<\/span><\/p>\n

The 10 Regression Algorithms in Machine Learning, Explained<\/b><\/h2>\n

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.<\/span><\/p>\n

1. Linear Regression<\/b><\/h3>\n

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 \u03b2.<\/span><\/p>\n

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.<\/span><\/p>\n

Use it when: <\/b>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.<\/span><\/p>\n

Avoid it when: <\/b>The pattern curves, features are strongly correlated with one another, or outliers are present: squared error makes it hypersensitive to extreme values.<\/span><\/p>\n

Indian use case: <\/b>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.<\/span><\/p>\n

scikit-learn: <\/b>LinearRegression()<\/span><\/p>\n

2. Polynomial Regression<\/b><\/h3>\n

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.<\/span><\/p>\n

Use it when: <\/b>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.<\/span><\/p>\n

Avoid it when: <\/b>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.<\/span><\/p>\n

Indian use case: <\/b>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.<\/span><\/p>\n

scikit-learn: <\/b>PolynomialFeatures() combined with LinearRegression()<\/span><\/p>\n

3. Ridge Regression (L2 Regularisation)<\/b><\/h3>\n

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.<\/span><\/p>\n

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.<\/span><\/p>\n

Use it when: <\/b>Features are correlated, you have more features than is comfortable relative to your row count, and you believe most features contribute something.<\/span><\/p>\n

Avoid it when: <\/b>You specifically want the model to discard irrelevant features \u2014 Ridge will not remove any.<\/span><\/p>\n

Indian use case: <\/b>Property price prediction where carpet area, built-up area and super built-up area are all recorded and mutually correlated by construction.<\/span><\/p>\n

scikit-learn: <\/b>Ridge(alpha=1.0)<\/span><\/p>\n

4. Lasso Regression (L1 Regularisation)<\/b><\/h3>\n

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.<\/span><\/p>\n

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.<\/span><\/p>\n

Use it when: <\/b>You have many features and suspect only a handful genuinely matter, and you want an automatically sparse, readable model.<\/span><\/p>\n

Avoid it when: <\/b>Correlated features form a meaningful group. Lasso tends to keep one arbitrarily and discard the rest, which can be misleading.<\/span><\/p>\n

Indian use case: <\/b>A retail chain with 300 candidate signals \u2014 footfall, weather, festival calendar, competitor pricing, SKU attributes narrowing down to the dozen that actually drive store-level revenue.<\/span><\/p>\n

scikit-learn: <\/b>Lasso(alpha=0.1)<\/span><\/p>\n

5. Elastic Net Regression<\/b><\/h3>\n

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.<\/span><\/p>\n

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.<\/span><\/p>\n

Use it when: <\/b>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.<\/span><\/p>\n

Avoid it when: <\/b>You have few features and no correlation problem \u2014 the extra hyperparameter is not worth tuning.<\/span><\/p>\n

Indian use case: <\/b>Credit scoring models built on bureau data, where dozens of derived variables such as utilisation ratios and enquiry counts move together.<\/span><\/p>\n

scikit-learn: <\/b>ElasticNet(alpha=0.1, l1_ratio=0.5)<\/span><\/p>\n

6. Decision Tree Regression<\/b><\/h3>\n

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.<\/span><\/p>\n

Trees need no feature scaling, handle non-linear relationships and interactions natively, and can be drawn on a whiteboard \u2014 which makes them the most explainable of the non-linear regression models in machine learning.<\/span><\/p>\n

Use it when: <\/b>You need a non-linear model that a non-technical audience can follow, or your features are on wildly different scales.<\/span><\/p>\n

Avoid it when: <\/b>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.<\/span><\/p>\n

Indian use case: <\/b>A microfinance institution estimating a sanctionable loan amount, where the branch manager must be able to justify the figure to a borrower.<\/span><\/p>\n

scikit-learn: <\/b>DecisionTreeRegressor(max_depth=5)<\/span><\/p>\n

7. Random Forest Regression<\/b><\/h3>\n

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.<\/span><\/p>\n

It is the strongest algorithm here that works well with almost no tuning, and it reports feature importances for free.<\/span><\/p>\n

Use it when: <\/b>You want strong accuracy quickly, your data is tabular and moderately sized, and you have limited time for hyperparameter search.<\/span><\/p>\n

Avoid it when: <\/b>You must explain individual predictions precisely, or you need to extrapolate beyond the range seen in training \u2014 tree ensembles cannot.<\/span><\/p>\n

Indian use case: <\/b>Health insurance premium estimation from age, city tier, BMI, pre-existing conditions and claim history, where interactions between variables matter.<\/span><\/p>\n

scikit-learn: <\/b>RandomForestRegressor(n_estimators=300)<\/span><\/p>\n

8. Gradient Boosting Regression (XGBoost, LightGBM, CatBoost)<\/b><\/h3>\n

This is the one the popular lists miss, and on structured data it is usually the most accurate option available.<\/span><\/p>\n

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.<\/span><\/p>\n

The three production libraries \u2014 XGBoost, LightGBM and CatBoost \u2014 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.<\/span><\/p>\n

Use it when: <\/b>Accuracy on tabular data is the priority and you can afford to tune learning rate, tree depth and number of estimators.<\/span><\/p>\n

Avoid it when: <\/b>Your data is very noisy and you cannot tune carefully \u2014 boosting will fit the noise. Always use early stopping on a validation set.<\/span><\/p>\n

Indian use case: <\/b>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.<\/span><\/p>\n

scikit-learn: <\/b>GradientBoostingRegressor() or HistGradientBoostingRegressor(); XGBRegressor() from the xgboost library<\/span><\/p>\n

9. Support Vector Regression (SVR)<\/b><\/h3>\n

SVR inverts the usual objective. Instead of minimising every error, it defines a tolerance margin \u2014 an epsilon-insensitive tube \u2014 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.<\/span><\/p>\n

Use it when: <\/b>You have a small to medium dataset with many features, and the relationship is non-linear but smooth.<\/span><\/p>\n

Avoid it when: <\/b>You have more than roughly 50,000 rows \u2014 training cost grows steeply \u2014 or your data is very noisy. Feature scaling is mandatory.<\/span><\/p>\n

Indian use case: <\/b>Short-horizon commodity price forecasting on a mandi where only a few hundred clean historical observations exist.<\/span><\/p>\n

scikit-learn: <\/b>SVR(kernel=’rbf’, C=1.0, epsilon=0.1)<\/span><\/p>\n

10. K-Nearest Neighbours Regression<\/b><\/h3>\n

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.<\/span><\/p>\n

Because everything depends on distance, feature scaling is not optional \u2014 an unscaled column measured in rupees will overwhelm one measured in years.<\/span><\/p>\n

Use it when: <\/b>The dataset is small, features are few, and local similarity is genuinely meaningful \u2014 comparable-property valuation is the classic case.<\/span><\/p>\n

Avoid it when: <\/b>You have many features. In high dimensions all points become roughly equidistant and the notion of a nearest neighbour breaks down.<\/span><\/p>\n

Indian use case: <\/b>Valuing a flat in a Bengaluru locality by averaging the per-square-foot rates of the most comparable recent sales nearby \u2014 precisely how a human valuer already works.<\/span><\/p>\n

scikit-learn: <\/b>KNeighborsRegressor(n_neighbors=5, weights=’distance’)<\/span><\/p>\n

Know More:\u00a0Best BCA Online Colleges in India (2026): The 7-Point Checklist to Choose the Right One<\/a><\/strong><\/p>\n

Two More You Will Meet in a Postgraduate Syllabus<\/b><\/h2>\n

Neural Network Regression<\/b><\/h3>\n

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 \u2014 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.<\/span><\/p>\n

Gaussian Process Regression<\/b><\/h3>\n

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 \u2014 scientific experiments, sensor calibration, Bayesian optimisation of hyperparameters. The cost is computational, and it becomes impractical beyond a few thousand rows.<\/span><\/p>\n

Master Comparison: All 10 Regression Algorithms in Machine Learning<\/b><\/h2>\n

This is the table to keep open while you work. It compares every algorithm above on the five properties that actually determine your choice.<\/span><\/p>\n

Table 2: Comparing the 10 Regression Algorithms Side by Side<\/b><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Algorithm<\/th>\nType<\/th>\nHandles
\ncollinearity<\/th>\n		Feature
\nselection<\/th>\n		Interpretability<\/th>\nBest dataset
\nprofile<\/th>\n<\/tr>\n<\/thead>\n
Linear Regression<\/td>\nLinear<\/td>\nNo<\/td>\nNo<\/td>\nVery high<\/td>\nSmall, clean, few
\nfeatures<\/td>\n<\/tr>\n
Polynomial
\nRegression<\/td>\n		Non-line
\nar<\/td>\n		No<\/td>\nNo<\/td>\nModerate<\/td>\nSmooth curved
\ntrend, one or two
\nfeatures<\/td>\n<\/tr>\n
Ridge Regression<\/td>\nLinear<\/td>\nYes<\/td>\nNo<\/td>\nHigh<\/td>\nCorrelated
\nfeatures, all
\nrelevant<\/td>\n<\/tr>\n
Lasso Regression<\/td>\nLinear<\/td>\nPartly<\/td>\nYes<\/td>\nHigh<\/td>\nMany features, few
\ntruly matter<\/td>\n<\/tr>\n
Elastic Net<\/td>\nLinear<\/td>\nYes<\/td>\nYes<\/td>\nHigh<\/td>\nWide data with
\ncorrelated groups<\/td>\n<\/tr>\n
Decision Tree<\/td>\nNon-line
\nar<\/td>\n		Yes<\/td>\nImplicit<\/td>\nHigh if shallow<\/td>\nMixed types, clear
\nrule structure<\/td>\n<\/tr>\n
Random Forest<\/td>\nNon-line
\nar<\/td>\n		Yes<\/td>\nImplicit<\/td>\nLow to moderate<\/td>\nMedium tabular,
\nminimal tuning
\ntime<\/td>\n<\/tr>\n
Gradient Boosting<\/td>\nNon-line
\nar<\/td>\n		Yes<\/td>\nImplicit<\/td>\nLow<\/td>\nMedium to large
\ntabular, accuracy
\nfirst<\/td>\n<\/tr>\n
Support Vector
\nRegression<\/td>\n		Both<\/td>\nModerate<\/td>\nNo<\/td>\nLow<\/td>\nSmall,
\nhigh-dimensional,
\nsmooth<\/td>\n<\/tr>\n
K-Nearest
\nNeighbours<\/td>\n		Non-line
\nar<\/td>\n		No<\/td>\nNo<\/td>\nModerate<\/td>\nSmall,
\nlow-dimensional,
\nlocal patterns<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Read More\u00a0:<\/strong>\u00a0BCA or BTech: Which Course Should You Choose After Class 12?<\/strong><\/a><\/p>\n

How to Choose: A Decision Framework That Takes Sixty Seconds<\/b><\/h2>\n

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.<\/span><\/p>\n

Table 3: Matching Your Data Situation to the Right Algorithm<\/b><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
If your situation looks like this<\/th>\nStart with<\/th>\nThen try<\/th>\n<\/tr>\n<\/thead>\n
Fewer than 1,000 rows, under 10
\nfeatures, roughly linear<\/td>\n		Linear Regression<\/td>\nRidge if any correlation exists<\/td>\n<\/tr>\n
Scatter plot shows a clear smooth curve<\/td>\nPolynomial Regression
\n(degree 2)<\/td>\n		Gradient Boosting if the curve is
\nirregular<\/td>\n<\/tr>\n
More features than rows, or heavy
\ncorrelation<\/td>\n		Ridge or Elastic Net<\/td>\nLasso if you need a sparse
\nmodel<\/td>\n<\/tr>\n
Hundreds of features, most probably
\nuseless<\/td>\n		Lasso<\/td>\nElastic Net if features cluster<\/td>\n<\/tr>\n
Tabular data, 1,000 to 100,000 rows,
\naccuracy is the goal<\/td>\n		Gradient Boosting (XGBoost
\nor LightGBM)<\/td>\n		Random Forest as a robust
\nbaseline<\/td>\n<\/tr>\n
You must justify every prediction to a
\nregulator or examiner<\/td>\n		Linear Regression or a
\nshallow Decision Tree<\/td>\n		Ridge for stability<\/td>\n<\/tr>\n
Small dataset but you need confidence
\nintervals<\/td>\n		Gaussian Process
\nRegression<\/td>\n		Bayesian Ridge<\/td>\n<\/tr>\n
Under 5,000 rows, many features,
\nsmooth relationship<\/td>\n		Support Vector Regression<\/td>\nElastic Net<\/td>\n<\/tr>\n
Prediction should mirror similar past
\ncases<\/td>\n		K-Nearest Neighbours<\/td>\nRandom Forest<\/td>\n<\/tr>\n
Inputs include images, text or sequences<\/td>\nNeural Network Regression<\/td>\nGradient Boosting on extracted
\nfeatures<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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 \u2014 the extra complexity is buying you almost nothing, and the simpler model will be easier to deploy, explain and maintain.<\/span><\/p>\n

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.<\/span><\/p>\n

How to Tell Whether Your Model Is Any Good: The Four Metrics<\/b><\/h2>\n

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.<\/span><\/p>\n

Table 4: Regression Evaluation Metrics and When to Use Each<\/b><\/p>\n\n\n\n\n\n\n\n\n
Metric<\/th>\nWhat it measures<\/th>\nUse it when<\/th>\nWatch out for<\/th>\n<\/tr>\n<\/thead>\n
MAE (Mean
\nAbsolute Error)<\/td>\n		Average size of error, in the
\ntarget’s own units<\/td>\n		All errors matter equally
\nand outliers exist<\/td>\n		Does not flag occasional
\nvery large misses<\/td>\n<\/tr>\n
RMSE (Root
\nMean Squared
\nError)<\/td>\n		Square root of average
\nsquared error, same units as
\ntarget<\/td>\n		Large errors are
\ndisproportionately costly<\/td>\n		One extreme outlier can
\ndominate the score<\/td>\n<\/tr>\n
MAPE (Mean
\nAbsolute
\nPercentage
\nError)<\/td>\n		Average error as a
\npercentage of actual value<\/td>\n		You need a unit-free
\nnumber to show a
\nbusiness audience<\/td>\n		Breaks down when actual
\nvalues approach zero<\/td>\n<\/tr>\n
R2<\/sup> and Adjusted
\nR2<\/sup><\/td>\n		Share of variance in the
\ntarget explained by the
\nmodel<\/td>\n		Comparing models on the
\nsame dataset<\/td>\n		Plain R2<\/sup> always rises when
\nyou add features \u2014 use
\nAdjusted R2<\/sup> for that
\ncomparison<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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.<\/span><\/p>\n

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.<\/span><\/p>\n

Know More\u00a0:<\/strong>\u00a0How to Become a Software Developer After BCA: Skills, Courses, and Career Path<\/strong><\/a><\/p>\n

The Four Assumptions Behind Linear Regression<\/b><\/h2>\n

Linear regression in machine learning \u2014 along with its regularised cousins Ridge, Lasso and Elastic Net \u2014 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.<\/span><\/p>\n