Regression Methods: Complete Guide to Choosing the Right Model
Regression is the workhorse of predictive analytics. Whether you are forecasting revenue, estimating customer lifetime value, or identifying which features drive conversion, some form of regression is almost certainly the right starting point. The challenge is not whether to use regression -- it is which regression to use. Pick ordinary least squares when your features are correlated and you will get wildly unstable coefficients. Pick Lasso when you need all features in the model and you will watch it zero out variables you care about.
This guide maps eight regression methods to the problems they actually solve. Each section tells you when a method fits, when it breaks, and links to a full deep-dive article with implementation details. If you already know your problem type, jump to the comparison table. If you are starting from scratch, follow the decision flowchart at the bottom.
Quick Comparison
| Method | Best For | Key Feature | Guide |
|---|---|---|---|
| Linear Regression | Baseline predictions, interpretable models | Closed-form solution, no tuning required | Full guide |
| Ridge Regression | Multicollinearity, many correlated features | L2 penalty shrinks coefficients without eliminating them | Full guide |
| Lasso Regression | Feature selection, sparse models | L1 penalty drives irrelevant coefficients to exactly zero | Full guide |
| Elastic Net | Correlated features when you also need sparsity | Combines L1 + L2, handles grouped correlations | Full guide |
| Logistic Regression | Binary classification, probability estimation | Outputs calibrated probabilities, not raw scores | Full guide |
| Polynomial Regression | Nonlinear relationships with known curvature | Extends linear regression with polynomial terms | Full guide |
| Quantile Regression | Non-normal distributions, risk modeling | Models any percentile, not just the mean | Full guide |
| Negative Binomial | Count data with overdispersion | Handles variance > mean in count outcomes | Full guide |
When to Use Each Method
Linear Regression
Start here. If your target variable is continuous and your features have a roughly linear relationship with the outcome, ordinary least squares (OLS) gives you an interpretable baseline with zero hyperparameters to tune. It works best when you have more observations than features, low multicollinearity, and reasonably normal residuals. When the model starts overfitting or coefficients swing wildly, that is your signal to move to a regularized method. Read the full linear regression guide.
Ridge Regression
When features are correlated -- think revenue and units sold, or multiple demographic variables that move together -- OLS produces unstable coefficients that change dramatically with small data shifts. Ridge regression adds an L2 penalty that shrinks all coefficients toward zero proportionally, stabilizing predictions without dropping any features from the model. It is the right choice when you believe all features contribute some signal but need to tame variance. Read the full Ridge regression guide.
Lasso Regression
If you suspect that most of your features are noise, Lasso is the tool that proves it. Its L1 penalty forces irrelevant coefficients to exactly zero, giving you automatic feature selection baked into the fitting process. Use it when you have dozens or hundreds of features and need to identify the handful that actually matter. The tradeoff: when features are highly correlated, Lasso arbitrarily picks one and drops the rest. Read the full Lasso regression guide.
Elastic Net
Elastic Net combines the feature selection of Lasso with the stability of Ridge. It is the practical default when you have correlated feature groups and still want a sparse model. The mixing parameter alpha controls the balance between L1 and L2 penalties -- set it closer to 1 for more sparsity, closer to 0 for more Ridge-like behavior. If you are unsure whether to use Ridge or Lasso, Elastic Net is often the answer. Read the full Elastic Net guide.
Logistic Regression
Despite the name, logistic regression is a classification method. Use it when your target is binary (churn vs. retain, convert vs. bounce, fraud vs. legitimate). It outputs calibrated probabilities rather than just class labels, which makes it ideal when the cost of errors is asymmetric or when you need to set custom decision thresholds. It is also the go-to method when stakeholders need to understand exactly why the model made a prediction. Read the full logistic regression guide.
Polynomial Regression
When the relationship between a feature and your target clearly curves -- diminishing returns on ad spend, U-shaped satisfaction scores, exponential growth phases -- polynomial regression captures it by adding squared, cubed, or higher-order terms to a linear model. Keep the degree low (2 or 3) unless you have strong theoretical reasons and plenty of data. Higher degrees fit training data beautifully and predict new data terribly. Read the full polynomial regression guide.
Quantile Regression
Standard regression predicts the mean. But if your data is skewed, has heavy tails, or you care about worst-case scenarios, the mean is not the right summary. Quantile regression lets you model any percentile -- the median for a robust central estimate, the 90th percentile for capacity planning, or the 10th percentile for risk floors. It makes no assumptions about the error distribution, which means it handles outliers gracefully. Read the full quantile regression guide.
Negative Binomial Regression
When your target variable is a count -- support tickets per customer, website visits per session, defects per batch -- and the variance exceeds the mean (overdispersion), Poisson regression underestimates uncertainty. Negative binomial regression adds a dispersion parameter that models this extra variance correctly, giving you reliable confidence intervals and hypothesis tests. Check for overdispersion first; if variance roughly equals the mean, plain Poisson is fine. Read the full negative binomial guide.
Decision Flowchart
Related Topics
Regression does not exist in isolation. These techniques complement regression modeling at different stages of the pipeline.
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