predict.LiblineaR: Predictions with LiblineaR model

In LiblineaR: Linear Predictive Models Based on the LIBLINEAR C/C++ Library

Predictions with LiblineaR model

Description

The function applies a model (classification or regression) produced by the LiblineaR function to every row of a data matrix and returns the model predictions.

Usage

## S3 method for class 'LiblineaR'
predict(object, newx, proba = FALSE, decisionValues = FALSE, ...)

Arguments

object	Object of class "LiblineaR", created by LiblineaR.
newx	An n x p matrix containing the new input data. A vector will be transformed to a n x 1 matrix. Sparse matrices of class matrix.csr, matrix.csc and matrix.coo from package SparseM are accepted. Sparse matrices of class dgCMatrix, dgRMatrix or dgTMatrix from package Matrix are also accepted. Note that C code at the core of LiblineaR package corresponds to a row-based sparse format. Hence, dgCMatrix, dgTMatrix, matrix.csc and matrix.csr inputs are first transformed into matrix.csr or dgRMatrix formats, which requires small extra computation time.
proba	Logical indicating whether class probabilities should be computed and returned. Only possible if the model was fitted with type=0, type=6 or type=7, i.e. a Logistic Regression. Default is FALSE.
decisionValues	Logical indicating whether model decision values should be computed and returned. Only possible for classification models (type<10). Default is FALSE.
...	Currently not used

Value

By default, the returned value is a list with a single entry:

predictions

A vector of predicted labels (or values for regression).

If proba is set to TRUE, and the model is a logistic regression, an additional entry is returned:

probabilities

An n x k matrix (k number of classes) of the class probabilities. The columns of this matrix are named after class labels.

If decisionValues is set to TRUE, and the model is not a regression model, an additional entry is returned:

decisionValues

An n x k matrix (k number of classes) of the model decision values. The columns of this matrix are named after class labels.

Note

If the data on which the model has been fitted have been centered and/or scaled, it is very important to apply the same process on the newx data as well, with the scale and center values of the training data.

Author(s)

Thibault Helleputte [e-mail] and
Jerome Paul [e-mail] and Pierre Gramme.
Based on C/C++-code by Chih-Chung Chang and Chih-Jen Lin

References

• For more information on LIBLINEAR itself, refer to:
  R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin.
  LIBLINEAR: A Library for Large Linear Classification,
  Journal of Machine Learning Research 9(2008), 1871-1874.
  https://www.csie.ntu.edu.tw/~cjlin/liblinear/

See Also

LiblineaR

LiblineaR documentation built on May 29, 2024, 7:13 a.m.