predict.LiblineaR

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Predictions with LiblineaR model

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.

Keywords
multivariate, classes, models, regression, classif, optimize
Usage
# S3 method for 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. A sparse matrix (from SparseM package) will also work.
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.

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. http://www.csie.ntu.edu.tw/~cjlin/liblinear

See Also

LiblineaR

Aliases
Documentation reproduced from package LiblineaR, version 2.10-8, License: GPL-2

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