Prediction: Prediction

In mRcSchwering/abacus: abacus

Description

This function trains a shrinkage discriminant analysis (sda) classifier using James-Stein-type shrinkage estimation. It returns the trained model, a feature ranking and a data.frame describing the features used for the model.

Usage

Prediction(model, abt, feats, ref, verb = FALSE)

Arguments

model	sda object containing a sda model
abt	num matrix containing test data with rows as observations and columns as features
feats	data.frame with columns name and value which identifies the features (columns) of the test data
ref	data.frame with columns name and value which identifies the features (columns) expected by the model
verb	bool (=FALSE) verbose, if true sda predict messages will be printed

Details

This function uses the sda package for training and ranking of a sda classifier. Shrinkage intensity for correlation matrix, variances, and frequencies is estimated from the data. With diag set to TRUE only the diagonal of the covariance matrix is used. This speeds up the process and uses less memory.

Value

list of 3 objects

• class factor containing predicted classes for test data

• posterior num matrix containing posterior probabilities of each class for test data

See Also

Other machine learning: CV, Convert, FeatureExtraction, Training

Examples

abt1 <- matrix(sample(0:1, 1000*100, replace = TRUE), 1000, 100)
feats1 <- data.frame(name = "test", value = 1:100)
labs1 <- sample(0:1, 1000, replace = TRUE)
model <- Training(abt1, labs1, feats1, n_max = 20)
str(model)
abt2 <- matrix(sample(0:1, 1000*100, replace = TRUE), 1000, 100)
feats2 <- data.frame(name = "test", value = 100:1)
labs2 <- sample(0:1, 1000, replace = TRUE)
pred <- Prediction(model$Model, abt2, feats2, model$FeatureList)
str(pred)
sum(pred$class == labs2)

mRcSchwering/abacus documentation built on May 21, 2019, 9:18 a.m.