This function is a wrapper over the predict function. The wrapper returns a single numeric score for each new observation. To do this it uses different extraction techniques for models from different classes, like for classification random forest is forces the output to be probabilities not classes itself.
yhat(X.model, newdata, ...)# S3 method for lm
yhat(X.model, newdata, ...)
# S3 method for randomForest
yhat(X.model, newdata, ...)
# S3 method for svm
yhat(X.model, newdata, ...)
# S3 method for glm
yhat(X.model, newdata, ...)
# S3 method for cv.glmnet
yhat(X.model, newdata, ...)
# S3 method for glmnet
yhat(X.model, newdata, ...)
# S3 method for ranger
yhat(X.model, newdata, ...)
# S3 method for WrappedModel
yhat(X.model, newdata, ...)
# S3 method for model_fit
yhat(X.model, newdata, ...)
# S3 method for train
yhat(X.model, newdata, ...)
# S3 method for default
yhat(X.model, newdata, ...)
# S3 method for H2ORegressionModel
yhat(X.model, newdata, ...)
# S3 method for H2OBinomialModel
yhat(X.model, newdata, ...)
object - a model to be explained
data.frame or matrix - observations for prediction
other parameters that will be passed to the predict function
An numeric matrix of predictions
Currently supported packages are:
class `cv.glmnet` and `glmnet` - models created with `glmnet` package
class `glm` - generalized linear models
class `H2OBinomialModel` and `H2ORegressionModel` - models created with `h2o` package
class `model_fit` - models created with `parsnip` package
class `lm` - linear models created with `stats::lm`
class `ranger` - models created with `ranger` package
class `randomForest` - random forest models created with `randomForest` package
class `svm` - support vector machines models created with the `e1071` package
class `train` - models created with `caret` package
class `WrappedModel` - models created with `mlr` package