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
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On this page
  • Single Hidden NN
  • Input Data
  • DNN
  • Input Data
  • Stuttgart Neural Network
  • Multi Layer Perceptron, Elman Network, Jordan Network
  • Input Data
  • Workflow Example
  • R Package

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  1. R
  2. Machine Learning

Neural Network based ML

Single Hidden NN, DNN, Stuttgart Neural Network

PreviousDimension ReductionNextSimilarity based ML

Last updated 4 years ago

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Single Hidden NN

Input Data

Parameters

Input

*Target Data

Input data

*Model Name

Name of the model (fill out at your discretion)

*Output Name

Name of the output after calculation

Merge Data

If not empty, the prediction data will be stored in the selected data frame(optional)

*X (all data type)

User-selected columns to be X

  • one or more columns should be selected

*Y

User-selected column to be Y

  • Only one column should be selected

DNN

Input Data

Parameters

Input

*Target Data

Input data

*Model Name

Name of the model (fill out at your discretion)

*Output Name

Name of the output after calculation

Merge Data

If not empty, the prediction data will be stored in the selected data frame(optional)

*X

User-selected columns to be X

  • At least one numeric/complex column should be selected

*Y

User-selected column to be Y

  • Only one column should be selected

Stuttgart Neural Network

Multi Layer Perceptron, Elman Network, Jordan Network

Input Data

Parameters

Input

*Target Data

Input data

*Model Name

Name of the model (fill out at your discretion)

*Output Name

Name of the output after calculation

Merge Data

If not empty, the prediction data will be stored in the selected data frame(optional)

*X (all data type)

User-selected columns to be X

  • At least one column should be selected

*Y

User-selected column to be Y

  • Only one column should be selected

Workflow Example

R Package

Single Hidden NN:

DNN:

Multi Layer Perceptron:

Elman Network:

Jordan Network:

https://www.rdocumentation.org/packages/nnet/versions/7.3-14/topics/nnet
https://www.rdocumentation.org/packages/neuralnet/versions/1.44.2/topics/neuralnet
https://www.rdocumentation.org/packages/RSNNS/versions/0.4-12/topics/mlp
https://rdrr.io/cran/RSNNS/man/elman.html
https://rdrr.io/cran/RSNNS/man/jordan.html
[Task Information of Single Hidden NN]
[Task Information of DNN]
[Task Information of Elman Network]
[Workflow Example of Single Hidden NN]