Feature selection methods are systematic techniques to pick the most informative variables for a predictive model while removing noise, redundancy, and leakage. They improve generalization, speed up training, and simplify interpretation across classical and modern machine learning. This article maps the landscape across filter, wrapper, and embedded approaches, clarifying when to use each and how to avoid common pitfalls like target leakage or multicollinearity. By the end, you will know how to combine quick statistical screens with model based searches to build compact, high performing feature sets. We focus on the Top 10 Feature Selection Methods practitioners rely on in real projects.
#1 Variance Threshold Filter
Variance Threshold Filter focuses on removing features with near zero variance because they contribute little discriminatory power. You begin by standardizing or appropriately scaling variables, then compute variance for each feature on the training set only to avoid leakage. Low variance can signal constant sensors, one hot encoded columns that never fire, or text n grams that almost never appear. Dropping these reduces dimensionality and speeds downstream methods. Choose the threshold by inspecting distributions and considering business meaning, since rare binary indicators might still be valuable. Pair this step with stratified splits so class imbalance does not hide useful variability.
#2 Correlation Thresholding and Deduplication
Correlation Thresholding removes redundant features that carry overlapping information. Compute pairwise correlation on numeric features using Pearson for linear relations and Spearman for monotonic relations. When absolute correlation exceeds a chosen threshold, keep the variable that is cheaper to collect, more stable in time, or easier to explain, and drop the rest. For categorical variables, use Cramers V or the correlation ratio with discretized targets. Run this after basic cleaning and imputation, and always base decisions on the training fold to avoid leakage. This step reduces multicollinearity, stabilizes linear models, and makes importances easier to interpret.
#3 Univariate Statistical Tests F test and Chi square
Univariate Statistical Tests rank features individually by their relationship with the target. For regression, the ANOVA F test highlights variables with strong linear signal. For classification with non negative counts or frequencies, the chi square test evaluates independence between each feature and class labels. SelectKBest or SelectPercentile utilities make it simple to keep the top scoring features. Because each feature is evaluated in isolation, interactions are ignored, so combine this with later model based steps. Apply cross validated scoring or nested validation to tune the number kept, and ensure preprocessing is fitted only on training data.
#4 Mutual Information Ranking
Mutual Information measures the reduction in uncertainty about the target when a feature is known, capturing both linear and nonlinear dependencies. It works for discrete and continuous variables through estimators that bin or use k nearest neighbors. Compared with simple correlation, mutual information can surface variables with curved or thresholded effects. Use mutual information to create a ranked shortlist, then validate with a model because it remains univariate and can be biased by discretization choices. Estimate on the training fold and use repeated cross validation to stabilize rankings when samples are limited or features are noisy.
#5 ReliefF and Neighborhood Based Filters
ReliefF and related algorithms score features by how well they differentiate neighboring instances of different classes while remaining consistent for neighbors of the same class. They capture local interactions better than simple filters and are robust to noisy or redundant variables. The algorithm samples instances, finds nearest hits and misses, and updates feature weights based on differences. Use ReliefF for medium sized tabular classification and consider SURF or MultiSURF for improved neighborhood handling. Since results depend on distance metrics, scale features and choose an appropriate k. Validate selected subsets with downstream models to confirm real performance gains.
#6 Recursive Feature Elimination with Cross Validation
Recursive Feature Elimination with cross validation is a wrapper method that repeatedly trains a model, ranks features by importance, and removes the weakest until a target size is reached. It directly optimizes for model performance using the chosen estimator, such as linear models, gradient boosting, or support vector machines. Use RFECV to search the number of features automatically under cross validation, protecting against overfitting. Because it retrains many times, ensure efficient pipelines, caching, and parallelism. Always nest this selection inside cross validation when also tuning model hyperparameters to avoid optimistic bias in reported scores. Document all dropped features to maintain transparency and support downstream audits.
#7 Sequential Forward and Backward Selection
Sequential Feature Selection explores subsets by adding or removing one feature at a time guided by cross validated performance. Sequential forward selection starts from none and adds the feature that most improves the score at each step. Sequential backward selection starts from all and removes the least useful feature at each step. It can capture interactions that univariate filters miss, while being simpler than exhaustive search. Set stopping criteria, evaluation metric, and maximum subset size upfront to control runtime. Use stratified folds for classification and group aware splits when observations share entities, so results generalize to new groups reliably.
#8 Genetic Algorithm and Heuristic Search
Genetic Algorithm based selection treats feature subsets as chromosomes, evolving populations through selection, crossover, and mutation to maximize cross validated performance. This stochastic search can escape local optima and discover compact interacting subsets, especially when the model is nonlinear. Define a fitness function that includes both accuracy and a penalty on subset size to favor parsimony. Constrain population sizes and generations to control compute, and fix random seeds to aid reproducibility. Use repeated runs to assess stability of discovered features. As with other wrappers, nest the entire search in cross validation and hold out a final untouched test set.
#9 L1 Regularization and Elastic Net
L1 regularization drives some coefficients exactly to zero, performing embedded selection during model training. Lasso regression and sparse logistic regression are common choices, while Elastic Net blends L1 and L2 to handle groups of correlated variables more gracefully. Standardize features, run a cross validated path over regularization strengths, and prefer nested validation when hyperparameters are tuned alongside preprocessing. Inspect coefficient stability across folds and random seeds to gauge robustness. Selected features tend to be predictive and simple to explain, which helps compliance and deployment. Beware data leakage, and fit scalers and encoders only on training folds before estimating penalties.
#10 Tree Based Importance, Permutation, and Boruta
Tree based models offer embedded importance through split gains and impurity reductions that account for nonlinearities and interactions. Gradient boosting and random forests can rank features reliably when hyperparameters are tuned and categorical variables are encoded well. Permutation importance provides an additional model agnostic check by measuring performance drops when a feature is shuffled. Boruta wraps a random forest with shadow features to test whether real variables are significantly more important than noise, yielding a conservative selection. Calibrate importance with cross validation and guard against bias toward high cardinality features. Finalize by retraining the model on the selected subset and validating on a holdout.