This function is a wrapper over various predict functions for different models and differnt model structures. 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 class 'lm'yhat(X.model, newdata,...)## S3 method for class 'randomForest'yhat(X.model, newdata,...)## S3 method for class 'svm'yhat(X.model, newdata,...)## S3 method for class 'gbm'yhat(X.model, newdata,...)## S3 method for class 'glm'yhat(X.model, newdata,...)## S3 method for class 'cv.glmnet'yhat(X.model, newdata,...)## S3 method for class 'glmnet'yhat(X.model, newdata,...)## S3 method for class 'ranger'yhat(X.model, newdata,...)## S3 method for class 'model_fit'yhat(X.model, newdata,...)## S3 method for class 'train'yhat(X.model, newdata,...)## S3 method for class 'lrm'yhat(X.model, newdata,...)## S3 method for class 'rpart'yhat(X.model, newdata,...)## S3 method for class '`function`'yhat(X.model, newdata,...)## S3 method for class 'party'yhat(X.model, newdata,...)## Default S3 method: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
Returns
An numeric matrix of predictions
Details
Currently supported packages are:
class cv.glmnet and glmnet - models created with glmnet package,
class glm - generalized linear models created with glm ,
class model_fit - models created with parsnip package,
class lm - linear models created with 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,