Cubist

method = 'cubist'

Type: Regression

Tuning Parameters: committees (#Committees), neighbors (#Instances)

Greedy Prototype Selection

method = 'protoclass'

Type: Classification

Tuning Parameters: eps (Ball Size), Minkowski (Distance Order)

k-Nearest Neighbors

method = 'kknn'

Type: Regression, Classification

Tuning Parameters: kmax (Max. #Neighbors), distance (Distance), kernel (Kernel)

k-Nearest Neighbors

method = 'knn'

Type: Classification, Regression

Tuning Parameters: k (#Neighbors)

Learning Vector Quantization

method = 'lvq'

Type: Classification

Tuning Parameters: size (Codebook Size), k (#Prototypes)

Nearest Shrunken Centroids

method = 'pam'

Type: Classification

Tuning Parameters: threshold (Shrinkage Threshold)

Random k-Nearest Neighbors

method = 'rknn'

Type: Classification, Regression

Tuning Parameters: k (#Neighbors), mtry (#Randomly Selected Predictors)

Random k-Nearest Neighbors with Feature Selection

method = 'rknnBel'

Type: Classification, Regression

Tuning Parameters: k (#Neighbors), mtry (#Randomly Selected Predictors), d (#Features Dropped)

• Links

  train Model List

• Topics

  • Data Sets
  • Visualizations
  • Pre-Processing
  • Data Splitting
  • Miscellaneous Model Functions
  • Model Training and Tuning
  • train Model List
  • train Models By Tag
  • train Models By Similarity
  • Using Custom Models
  • Variable Importance
  • Feature Selection
  • Other Functions
  • Parallel Processing