- 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 - Forward Feature Construction
Forward Feature Construction is a feature selection method in machine learning where we start with an empty set of features and iteratively add the best performing feature at each step until the desired number of features is reached.
The goal of feature selection is to identify the most important features that are relevant for predicting the target variable, while ignoring the less important features that add noise to the model and may lead to overfitting.
The steps involved in Forward Feature Construction are as follows −
Initialize an empty set of features.
Set the maximum number of features to be selected.
-
Iterate until the desired number of features is reached −
For each remaining feature that is not already in the set of selected features, fit a model with the selected features and the current feature, and evaluate its performance using a validation set.
Select the feature that leads to the best performance and add it to the set of selected features.
Return the set of selected features as the optimal set for the model.
The key advantage of Forward Feature Construction is that it is computationally efficient and can be used for high-dimensional datasets. However, it may not always lead to the optimal set of features, especially if there are highly correlated features or non-linear relationships between the features and the target variable.
Example
Here is an example to implement Forward Feature Construction in Python −
# Importing the necessary libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# 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
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
# Create an empty set of features
selected_features = set()
# Set the maximum number of features to be selected
max_features = 8
# Iterate until the desired number of features is reached
while len(selected_features) < max_features:
# Set the best feature and the best score to be 0
best_feature = None
best_score = 0
# Iterate over all the remaining features
for i in range(X_train.shape[1]):
# Skip the feature if it's already selected
if i in selected_features:
continue
# Select the current feature and fit a linear regression model
X_train_selected = X_train[:, list(selected_features) + [i]]
regressor = LinearRegression()
regressor.fit(X_train_selected, y_train)
# Compute the score on the testing set
X_test_selected = X_test[:, list(selected_features) + [i]]
score = regressor.score(X_test_selected, y_test)
# Update the best feature and score if the current feature performs better
if score > best_score:
best_feature = i
best_score = score
# Add the best feature to the set of selected features
selected_features.add(best_feature)
# Print the selected features and the score
print('Selected Features:', list(selected_features))
print('Score:', best_score)
Output
On execution, it will produce the following output −
Selected Features: [1] Score: 0.23530716168783583 Selected Features: [0, 1] Score: 0.2923143573608237 Selected Features: [0, 1, 5] Score: 0.3164103491569179 Selected Features: [0, 1, 5, 6] Score: 0.3287368302427327 Selected Features: [0, 1, 2, 5, 6] Score: 0.334586804842275 Selected Features: [0, 1, 2, 3, 5, 6] Score: 0.3356264736550455 Selected Features: [0, 1, 2, 3, 4, 5, 6] Score: 0.3313166516703744 Selected Features: [0, 1, 2, 3, 4, 5, 6, 7] Score: 0.32230203252064216