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 catboost.Model
yhat(X.model, newdata, ...)

# S3 method for H2ORegressionModel
yhat(X.model, newdata, ...)

# S3 method for H2OBinomialModel
yhat(X.model, newdata, ...)

Arguments

X.model

object - a model to be explained

newdata

data.frame or matrix - observations for prediction

...

other parameters that will be passed to the predict function

Value

An numeric matrix of predictions

Details

Currently supported packages are:

  • class `catboost.Model` - models created with `catboost` package

  • 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