R-Flow
  • R-Flow Task Guide
  • User-Guide
  • R
    • Transfer: R to Python
    • Script: R Script
    • Data Preprocess
      • Data Cleansing (Missing, Duplicate Data)
        • Missing Data Imputer
        • Outlier Imputer
      • Data Scaler
      • Normalize
      • Encode Feature
      • Highly Imbalanced Data
      • Data Handling
        • Data Aggregate
        • Data Subset
        • Data Filter
        • Data Join
        • Data Merge
        • Data Sort
        • Data Sampling
        • Data Imputation
    • Statistics
      • Hypothesis Test
      • ANOVA
      • PCR
      • Time Series Analysis
      • Factor Analysis
    • Machine Learning
      • Feature Selection
        • Filter Methods
        • Wrapper Methods
      • Dimension Reduction
      • Neural Network based ML
      • Similarity based ML
      • Information based ML
      • Bayesian Based ML
      • Clustering
        • Optimal
        • OPTICS
        • Others
      • Outlier Detection
        • Univariate Outliers
        • Bivariate Outliers
        • Multivariate Outliers
        • Time-Series Outliers
      • Recommend
      • Association Rule Analysis
    • Data Predict
    • R Object Load, Save
  • Python
    • Transfer: Python To R
    • Script: Python Script
    • Data Preprocess
      • Data Cleansing
        • Missing Data Imputer
      • Data Scaler
      • Normalize
      • Encode Feature
      • Highly-Imbalanced Data
        • Under-Sampling
        • Over-Sampling
        • Combination of over- and under-sampling
      • Data Handling
        • Data Aggregate
        • Data X,Y Split
        • Data Filter
        • Data Join
        • Data Concat
        • Data Sampling
        • Data Imputation
    • Machine Learning
      • Estimator
      • Probability Calibration
      • Clustering
      • Matrix Decomposition
      • Discriminant Analysis
      • Ensemble Methods
      • Feature Selection
      • Isotonic regression
      • Kernel Ridge Regression
      • Linear Models
        • Linear classifiers
        • Classical linear regressors
        • Regressors with variable selection
        • Bayesian Reg.
        • Multi-task linear regressors with variable selection
        • Outlier-robust regressors
      • Manifold Learning
      • Gaussian Mixture Models
      • Model Selection
      • Multiclass and Multilabel Classification
      • Multioutput regression and classification
      • Naive Bayes
      • Nearest Neighbors
      • Neural network models
      • Support Vector Machines
    • Data Predict/Transform
      • Data Transform
      • Python Predict
    • Python Object Load,Save
Powered by GitBook
On this page
  • Recursive Feature Elimination
  • Input Data
  • Wrapper Algorithm
  • Input Data
  • Workflow Example
  • R Package

Was this helpful?

  1. R
  2. Machine Learning
  3. Feature Selection

Wrapper Methods

Recursive Feature Elimination, Wrapper Algorithm

PreviousFilter MethodsNextDimension Reduction

Last updated 4 years ago

Was this helpful?

Recursive Feature Elimination

Input Data

Parameters

Input

Target Data

Input data

rfe object

Name of the recursive feature elimination object (fill out at your discretion)

X

User-defined columns for X

  • supply two or more columns (all data type)

Y

User-defined column of Y

  • factor for classification, numeric vector for regression

Wrapper Algorithm

Input Data

Parameters

Input

Target Data

Input data

Boruta Object

Name of the boruta object (fill out at your discretion)

X

User-defined columns for X (predictors)

  • supply two or more columns (all data type)

Y

User-defined column of Y

  • factor for classification, numeric vector for regression

Workflow Example

R Package

Recursive Feature Elimination: https://www.rdocumentation.org/packages/caret/versions/6.0-86/topics/rfe

Wrapper Algorithm: https://www.rdocumentation.org/packages/Boruta/versions/7.0.0/topics/Boruta

[Task Information of RecursiveFeatureElimination]
[Task Information of Wrapper Algorithm]