- ML - Home
- ML - Introduction
- ML - Getting Started
- ML - Basic Concepts
- ML - Ecosystem
- ML - Python Libraries
- ML - Applications
- ML - Life Cycle
- ML - Required Skills
- ML - Implementation
- ML - Challenges & Common Issues
- ML - Limitations
- ML - Reallife Examples
- ML - Data Structure
- ML - Mathematics
- ML - Artificial Intelligence
- ML - Neural Networks
- ML - Deep Learning
- ML - Getting Datasets
- ML - Categorical Data
- ML - Data Loading
- ML - Data Understanding
- ML - Data Preparation
- ML - Models
- ML - Supervised Learning
- ML - Unsupervised Learning
- ML - Semi-supervised Learning
- ML - Reinforcement Learning
- ML - Supervised vs. Unsupervised
- Machine Learning Data Visualization
- ML - Data Visualization
- ML - Histograms
- ML - Density Plots
- ML - Box and Whisker Plots
- ML - Correlation Matrix Plots
- ML - Scatter Matrix Plots
- Statistics for Machine Learning
- ML - Statistics
- ML - Mean, Median, Mode
- ML - Standard Deviation
- ML - Percentiles
- ML - Data Distribution
- ML - Skewness and Kurtosis
- ML - Bias and Variance
- ML - Hypothesis
- Regression Analysis In ML
- ML - Regression Analysis
- ML - Linear Regression
- ML - Simple Linear Regression
- ML - Multiple Linear Regression
- ML - Polynomial Regression
- Classification Algorithms In ML
- ML - Classification Algorithms
- ML - Logistic Regression
- ML - K-Nearest Neighbors (KNN)
- ML - Naïve Bayes Algorithm
- ML - Decision Tree Algorithm
- ML - Support Vector Machine
- ML - Random Forest
- ML - Confusion Matrix
- ML - Stochastic Gradient Descent
- Clustering Algorithms In ML
- ML - Clustering Algorithms
- ML - Centroid-Based Clustering
- ML - K-Means Clustering
- ML - K-Medoids Clustering
- ML - Mean-Shift Clustering
- ML - Hierarchical Clustering
- ML - Density-Based Clustering
- ML - DBSCAN Clustering
- ML - OPTICS Clustering
- ML - HDBSCAN Clustering
- ML - BIRCH Clustering
- ML - Affinity Propagation
- ML - Distribution-Based Clustering
- ML - Agglomerative Clustering
- Dimensionality Reduction In ML
- ML - Dimensionality Reduction
- ML - Feature Selection
- ML - Feature Extraction
- ML - Backward Elimination
- ML - Forward Feature Construction
- ML - High Correlation Filter
- ML - Low Variance Filter
- ML - Missing Values Ratio
- ML - Principal Component Analysis
- Reinforcement Learning
- ML - Reinforcement Learning Algorithms
- ML - Exploitation & Exploration
- ML - Q-Learning
- ML - REINFORCE Algorithm
- ML - SARSA Reinforcement Learning
- ML - Actor-critic Method
- ML - Monte Carlo Methods
- ML - Temporal Difference
- Deep Reinforcement Learning
- ML - Deep Reinforcement Learning
- ML - Deep Reinforcement Learning Algorithms
- ML - Deep Q-Networks
- ML - Deep Deterministic Policy Gradient
- ML - Trust Region Methods
- Quantum Machine Learning
- ML - Quantum Machine Learning
- ML - Quantum Machine Learning with Python
- Machine Learning Miscellaneous
- ML - Performance Metrics
- ML - Automatic Workflows
- ML - Boost Model Performance
- ML - Gradient Boosting
- ML - Bootstrap Aggregation (Bagging)
- ML - Cross Validation
- ML - AUC-ROC Curve
- ML - Grid Search
- ML - Data Scaling
- ML - Train and Test
- ML - Association Rules
- ML - Apriori Algorithm
- ML - Gaussian Discriminant Analysis
- ML - Cost Function
- ML - Bayes Theorem
- ML - Precision and Recall
- ML - Adversarial
- ML - Stacking
- ML - Epoch
- ML - Perceptron
- ML - Regularization
- ML - Overfitting
- ML - P-value
- ML - Entropy
- ML - MLOps
- ML - Data Leakage
- ML - Monetizing Machine Learning
- ML - Types of Data
- Machine Learning - Resources
- ML - Quick Guide
- ML - Cheatsheet
- ML - Interview Questions
- ML - Useful Resources
- ML - Discussion
Machine Learning - Low Variance Filter
Low Variance Filter is a feature selection technique used in machine learning to identify and remove low variance features from the dataset. This technique is used to improve the performance of the model by reducing the number of features used for training the model and to remove the features that have little or no discriminatory power.
The Low Variance Filter works by computing the variance of each feature in the dataset and removing the features that have a variance below a certain threshold. This is done because features with low variance have little or no discriminatory power and are unlikely to be useful for predicting the target variable.
The steps involved in implementing Low Variance Filter are as follows −
Compute the variance of each feature in the dataset.
Set a threshold for the variance of the features.
Remove the features that have a variance below the threshold.
Use the remaining features for training the machine learning model.
Example
Here is an example to implement Low Variance Filter in Python −
# Importing the necessary libraries
import pandas as pd
import numpy as np
# Load the diabetes dataset
diabetes = pd.read_csv(r'C:\Users\Leekha\Desktop\diabetes.csv')
# Define the predictor variables (X) and the target variable (y)
X = diabetes.iloc[:, :-1].values
y = diabetes.iloc[:, -1].values
# Compute the variance of each feature
variances = np.var(X, axis=0)
# Set the threshold for the variance of the features
threshold = 0.1
# Find the indices of the low variance features
low_var_indices = np.where(variances < threshold)
# Remove the low variance features from the dataset
X_filtered = np.delete(X, low_var_indices, axis=1)
# Print the shape of the filtered dataset
print('Shape of the filtered dataset:', X_filtered.shape)
Output
When you execute this code, it will produce the following output −
Shape of the filtered dataset: (768, 8)
Advantages of Low Variance Filter
Following are the advantages of using Low Variance Filter −
Reduces overfitting − The Low Variance Filter can help reduce overfitting by removing features that do not contribute much to the prediction of the target variable.
Saves computational resources − With fewer features, the computational resources required to train machine learning models are reduced.
Improves model performance − By removing low variance features, the Low Variance Filter can improve the performance of machine learning models.
Simplifies the model − With fewer features, the model can be easier to interpret and understand.
Disadvantages of Low Variance Filter
Following are the disadvantages of using Low Variance Filter −
Information loss − The Low Variance Filter can lead to information loss because it removes features that may contain important information.
Affects non-linear relationships − The Low Variance Filter assumes that the relationships between the features are linear. It may not work well for datasets where the relationships between the features are non-linear.
Impact on the dependent variable − Removing low variance features can sometimes have a negative impact on the dependent variable, particularly if the features are important for predicting the dependent variable.
Selection bias − The Low Variance Filter may introduce selection bias if it removes features that are important for predicting the dependent variable.