Variance Threshold Last Updated : 23 Jul, 2025 Comments Improve Suggest changes Like Article Like Report Variance Threshold is a simple technique that removes all features whose variance does not meet a specified threshold. Variance in data represents how spread out the values of a feature are. Features with low variance (e.g., nearly constant values) contain little information because it remains almost constant across samples. Removing them helps reduce the noise and computational cost.For example, if a column in your dataset has the same value for 99% of the rows, it contributes very little in distinguishing between data points and therefore may be safely removed.Steps-by-step WorkingThe process of using the Variance Threshold method involves the following steps:Calculate variance for each feature in the dataset.Compare each variance to the predefined threshold.Discard features with variance below the threshold.Retain features with sufficient variability.This technique is unsupervised, meaning it does not consider the target labels when selecting features. It's most effective as a first-pass filter before applying more complex methods.Implementation with Scikit-learnPython's scikit-learn library offers a straightforward implementation of Variance Threshold: Python from sklearn.feature_selection import VarianceThreshold import numpy as np # Sample dataset: 5 samples, 4 features X = np.array([ [0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3], [0, 1, 0, 3], [0, 1, 3, 3] ]) # Initialize VarianceThreshold selector = VarianceThreshold() # Fit and transform the data X_sele = selector.fit_transform(X) print("Original shape:", X.shape) print("Reduced shape:", X_sele.shape) Output:Original shape: (5, 4)Reduced shape: (5, 2)In this example, two features are removed because their variance was zero or very low.You can also specify a different threshold: Python selector = VarianceThreshold(threshold=0.5) This removes features with variance less than 0.5.Use Cases and ApplicationsText Classification: High-dimensional sparse data (e.g., TF-IDF vectors) often contain many features with zero or low variance.Bioinformatics: Genomic data has thousands of gene expression levels, many of which vary little.Sensor Networks: Sensor readings may remain constant for long periods, introducing low-variance features.In all these domains, applying Variance Threshold can reduce noise and computation time.Advantages and LimitationsAdvantagesSimplicity: Very easy to understand and implement.Speed: Computationally efficient even on large datasets.Preprocessing Utility: Useful as a first step in the feature selection pipeline.LimitationsIgnores Target Variable: Cannot assess the relevance of a feature with respect to the output.Not Effective for All Low-Variance Features: Some low-variance features might still be important for classification.Fails with Redundant Features: Cannot detect multicollinearity or correlated features with high variance.Best Practices and TipsScale Your Data: Ensure features are scaled appropriately; otherwise, variance may be misleading.Visualize Before Selecting: Plot feature variance distribution before choosing a threshold.Combine with Other Methods: Use it with supervised feature selection techniques for better results.Domain Knowledge Matters: Retain important low-variance features if they're known to be significant.Similar ArticlesFeature Selection Techniques in Machine LearningFeature Selection in Python with Scikit-LearnOptimal feature selection for Support Vector MachinesHow to Perform Feature Selection for Regression DataPerforming Feature Selection with gridsearchcv in SklearnFeature selection using Decision Tree Comment More infoAdvertise with us V Vandita Gupta Improve Article Tags : Machine Learning AI-ML-DS Practice Tags : Machine Learning Similar Reads Machine Learning Tutorial Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. 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