- Machine Learning Basics
- Machine Learning - Home
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- Machine Learning - Supervised vs. Unsupervised
- Machine Learning Data Visualization
- Machine Learning - Data Visualization
- Machine Learning - Histograms
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- Machine Learning - Correlation Matrix Plots
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- Statistics for Machine Learning
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- Machine Learning - Mean, Median, Mode
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- Regression Analysis In ML
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- Machine Learning - Linear Regression
- Machine Learning - Simple Linear Regression
- Machine Learning - Multiple Linear Regression
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- Classification Algorithms In ML
- Machine Learning - Classification Algorithms
- Machine Learning - Logistic Regression
- Machine Learning - K-Nearest Neighbors (KNN)
- Machine Learning - Naïve Bayes Algorithm
- Machine Learning - Decision Tree Algorithm
- Machine Learning - Support Vector Machine
- Machine Learning - Random Forest
- Machine Learning - Confusion Matrix
- Machine Learning - Stochastic Gradient Descent
- Clustering Algorithms In ML
- Machine Learning - Clustering Algorithms
- Machine Learning - Centroid-Based Clustering
- Machine Learning - K-Means Clustering
- Machine Learning - K-Medoids Clustering
- Machine Learning - Mean-Shift Clustering
- Machine Learning - Hierarchical Clustering
- Machine Learning - Density-Based Clustering
- Machine Learning - DBSCAN Clustering
- Machine Learning - OPTICS Clustering
- Machine Learning - HDBSCAN Clustering
- Machine Learning - BIRCH Clustering
- Machine Learning - Affinity Propagation
- Machine Learning - Distribution-Based Clustering
- Machine Learning - Agglomerative Clustering
- Dimensionality Reduction In ML
- Machine Learning - Dimensionality Reduction
- Machine Learning - Feature Selection
- Machine Learning - Feature Extraction
- Machine Learning - Backward Elimination
- Machine Learning - Forward Feature Construction
- Machine Learning - High Correlation Filter
- Machine Learning - Low Variance Filter
- Machine Learning - Missing Values Ratio
- Machine Learning - Principal Component Analysis
- Machine Learning Miscellaneous
- Machine Learning - Performance Metrics
- Machine Learning - Automatic Workflows
- Machine Learning - Boost Model Performance
- Machine Learning - Gradient Boosting
- Machine Learning - Bootstrap Aggregation (Bagging)
- Machine Learning - Cross Validation
- Machine Learning - AUC-ROC Curve
- Machine Learning - Grid Search
- Machine Learning - Data Scaling
- Machine Learning - Train and Test
- Machine Learning - Association Rules
- Machine Learning - Apriori Algorithm
- Machine Learning - Gaussian Discriminant Analysis
- Machine Learning - Cost Function
- Machine Learning - Bayes Theorem
- Machine Learning - Precision and Recall
- Machine Learning - Adversarial
- Machine Learning - Stacking
- Machine Learning - Epoch
- Machine Learning - Perceptron
- Machine Learning - Regularization
- Machine Learning - Overfitting
- Machine Learning - P-value
- Machine Learning - Entropy
- Machine Learning - MLOps
- Machine Learning - Data Leakage
- Machine Learning - Resources
- Machine Learning - Quick Guide
- Machine Learning - Useful Resources
- Machine Learning - 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.