- Machine Learning Basics
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- Machine Learning Data Visualization
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- Statistics for Machine Learning
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- Regression Analysis In ML
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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
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- Machine Learning - Data Scaling
- Machine Learning - Train and Test
- Machine Learning - Association Rules
- Machine Learning - Apriori Algorithm
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- Machine Learning - Cost Function
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- 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 - 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)