- 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 - Feature Selection
Feature selection is an important step in machine learning that involves selecting a subset of the available features to improve the performance of the model. The following are some commonly used feature selection techniques −
Filter Methods
This method involves evaluating the relevance of each feature by calculating a statistical measure (e.g., correlation, mutual information, chi-square, etc.) and ranking the features based on their scores. Features that have low scores are then removed from the model.
To implement filter methods in Python, you can use the SelectKBest or SelectPercentile functions from the sklearn.feature_selection module. Below is a small code snippet to implement Feature selection.
from sklearn.feature_selection import SelectPercentile, chi2 selector = SelectPercentile(chi2, percentile=10) X_new = selector.fit_transform(X, y)
Wrapper Methods
This method involves evaluating the model's performance by adding or removing features and selecting the subset of features that yields the best performance. This approach is computationally expensive, but it is more accurate than filter methods.
To implement wrapper methods in Python, you can use the RFE (Recursive Feature Elimination) function from the sklearn.feature_selection module. Below is a small code snippet to implement Wrapper method.
from sklearn.feature_selection import RFE from sklearn.linear_model import LogisticRegression estimator = LogisticRegression() selector = RFE(estimator, n_features_to_select=5) selector = selector.fit(X, y) X_new = selector.transform(X)
Embedded Methods
This method involves incorporating feature selection into the model building process itself. This can be done using techniques such as Lasso regression, Ridge regression, or Decision Trees. These methods assign weights to each feature and features with low weights are removed from the model.
To implement embedded methods in Python, you can use the Lasso or Ridge regression functions from the sklearn.linear_model module. Below is a small code snippet for implementing embedded methods −
from sklearn.linear_model import Lasso lasso = Lasso(alpha=0.1) lasso.fit(X, y) coef = pd.Series(lasso.coef_, index = X.columns) important_features = coef[coef != 0]
Principal Component Analysis (PCA)
This is a type of unsupervised learning method that involves transforming the original features into a set of uncorrelated principal components that explain the maximum variance in the data. The number of principal components can be selected based on a threshold value, which can reduce the dimensionality of the dataset.
To implement PCA in Python, you can use the PCA function from the sklearn.decomposition module. For example, to reduce the number of features you can use PCA as given the following code −
from sklearn.decomposition import PCA pca = PCA(n_components=3) X_new = pca.fit_transform(X)
Recursive Feature Elimination (RFE)
This method involves recursively eliminating the least significant features until a subset of the most important features is identified. It uses a model-based approach and can be computationally expensive, but it can yield good results in high-dimensional datasets.
To implement RFE in Python, you can use the RFECV (Recursive Feature Elimination with Cross Validation) function from the sklearn.feature_selection module. For example, below is a small code snippet with the help of which we can implement to use Recursive Feature Elimination −
from sklearn.feature_selection import RFECV from sklearn.tree import DecisionTreeClassifier estimator = DecisionTreeClassifier() selector = RFECV(estimator, step=1, cv=5) selector = selector.fit(X, y) X_new = selector.transform(X)
These feature selection techniques can be used alone or in combination to improve the performance of machine learning models. It is important to choose the appropriate technique based on the size of the dataset, the nature of the features, and the type of model being used.
Example
In the below example, we will implement three feature selection methods − univariate feature selection using the chi-square test, recursive feature elimination with cross-validation (RFECV), and principal component analysis (PCA).
We will use the Breast Cancer Wisconsin (Diagnostic) Dataset, which is included in scikit-learn. This dataset contains 569 samples with 30 features, and the task is to classify whether a tumor is malignant or benign based on these features.
Here is the Python code to implement these feature selection methods on the Breast Cancer Wisconsin (Diagnostic) Dataset −
# Import necessary libraries and dataset import pandas as pd from sklearn.datasets import load_diabetes from sklearn.feature_selection import SelectKBest, chi2 from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Load the dataset diabetes = pd.read_csv(r'C:\Users\Leekha\Desktop\diabetes.csv') # Split the dataset into features and target variable X = diabetes.drop('Outcome', axis=1) y = diabetes['Outcome'] # Apply univariate feature selection using the chi-square test selector = SelectKBest(chi2, k=4) X_new = selector.fit_transform(X, y) # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.3, random_state=42) # Fit a logistic regression model on the selected features clf = LogisticRegression() clf.fit(X_train, y_train) # Evaluate the model on the test set accuracy = clf.score(X_test, y_test) print("Accuracy using univariate feature selection: {:.2f}".format(accuracy)) # Recursive feature elimination with cross-validation (RFECV) estimator = LogisticRegression() selector = RFECV(estimator, step=1, cv=5) selector.fit(X, y) X_new = selector.transform(X) scores = cross_val_score(LogisticRegression(), X_new, y, cv=5) print("Accuracy using RFECV feature selection: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2)) # PCA implementation pca = PCA(n_components=5) X_new = pca.fit_transform(X) scores = cross_val_score(LogisticRegression(), X_new, y, cv=5) print("Accuracy using PCA feature selection: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2)) Output
When you execute this code, it will produce the following output on the terminal −
Accuracy using univariate feature selection: 0.74 Accuracy using RFECV feature selection: 0.77 (+/- 0.03) Accuracy using PCA feature selection: 0.75 (+/- 0.07)
