predict.citodnn: Predict from a fitted dnn model

In cito: Building and Training Neural Networks

Predict from a fitted dnn model

Description

Predict from a fitted dnn model

Usage

## S3 method for class 'citodnn'
predict(
  object,
  newdata = NULL,
  type = c("link", "response", "class"),
  device = c("cpu", "cuda", "mps"),
  reduce = c("mean", "median", "none"),
  ...
)

## S3 method for class 'citodnnBootstrap'
predict(
  object,
  newdata = NULL,
  type = c("link", "response", "class"),
  device = c("cpu", "cuda", "mps"),
  reduce = c("mean", "median", "none"),
  ...
)

Arguments

object	a model created by dnn
newdata	new data for predictions
type	type of predictions. The default is on the scale of the linear predictor, "response" is on the scale of the response, and "class" means that class predictions are returned (if it is a classification task)
device	device on which network should be trained on.
reduce	predictions from bootstrapped model are by default reduced (mean, optional median or none)
...	additional arguments

Value

prediction matrix

Examples

if(torch::torch_is_installed()){
library(cito)

set.seed(222)
validation_set<- sample(c(1:nrow(datasets::iris)),25)

# Build and train  Network
nn.fit<- dnn(Sepal.Length~., data = datasets::iris[-validation_set,])

# Use model on validation set
predictions <- predict(nn.fit, iris[validation_set,])
# Scatterplot
plot(iris[validation_set,]$Sepal.Length,predictions)
# MAE
mean(abs(predictions-iris[validation_set,]$Sepal.Length))
}

cito documentation built on May 29, 2024, 1:28 a.m.