Difference between revisions of "Classification analysis advanced (analysis)"

Latest revision as of 18:15, 9 December 2020

Analysis title

Classification analysis advanced

Provider

Institute of Systems Biology

Class

ClassificationAnalysisAdvanced

Plugin

biouml.plugins.machinelearning (Machine learning)

[edit] Description

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

[edit] 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