Classification analysis advanced (analysis)

Analysis title

Classification analysis advanced

Provider

Institute of Systems Biology

Class

ClassificationAnalysisAdvanced

Plugin

biouml.plugins.machinelearning (Machine learning)

Description

Create and save classification model or load classification model for prediction of response or cross-validation of classification model.

Parameters:

• Classification mode – Select classification mode
• Classification type – Select classification type
• Path to data matrix – Path to table or file with data matrix
• Variable names – Select variable names
• Response name – Select response name
• Path to folder with saved model – Path to folder with saved model
• Parameters for LDA-classification – Parameters for LDA-classification
  • Max number of rotations – Maximal number of rotations for calculation of inverse matrix or eigen vectors
  • Epsilon for rotations – Epsilon for calculation of inverse matrix or eigen vectors
  • Max number of iterations – Max number of iterations in Lyusternikm method for calculation of maximal eigen value and corresponding eigen vector
  • Epsilon for iterations in Lyusternik method – Epsilon for iterations in Lyusternik method
• Parameters for maximum likelihood classification – Please, determine parameters for maximum likelihood classification based on multinormal distribution
  • Max number of rotations – Maximal number of rotations for calculation of inverse matrix or eigen vectors
  • Epsilon for rotations – Epsilon for calculation of inverse matrix or eigen vectors
• Parameters for perceptron classification – Please, determine parameters for perceptron classification
  • Optimization type – Select optimization type
  • Max number of iterations – Max number of iterations
  • Admissible misclassification rate – Admissible misclassification rate
• Parameters for logistic regression – Please, determine parameters for logistic regression
  • Max number of iterations – Max number of iterations
  • Admissible misclassification rate – Admissible misclassification rate
  • Max number of rotations – Maximal number of rotations for calculation of inverse matrix or eigen vectors
  • Epsilon for rotations – Epsilon for calculation of inverse matrix or eigen vectors
• Parameters for cross-validation – Please, determine parameters for cross-validation
  • Percentage of data for training – Proportion (in %) of data for training
• Parameters for variable selection – Parameters for variable selection
  • Number of selected variables – Number of selected variables
  • Variable selection type – Please, determine variable selection type
• Path to output folder – Path to output folder