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machine-learningclassificationr-caret

Use tested machine learning model on new unlabeled single observation or dataset?

How can I use a trained and tested algorithm (eg. machine learning classifier) after being saved, on a new observation/dataset, whose I do not know the class (eg. ill vs healthy) based on predictors used for model training? I use caret but can't find any lines of code for this. many thanks

Solution

  • After training and testing any machine learning model you can save the model as .rds file and call it as

    #Save the fitted model as .rds file
saveRDS(model_fit, "model.rds")
my_model <- readRDS("model.rds")

    Creating a new observation from the same dataset or you can use a new dataset also

    new_obs <- iris[100,] #I am using default iris dataset, 100 no sample

    Prediction on the new observation

    predicted_new <- predict(my_model, new_obs)
confusionMatrix(reference = new_obs$Species, data = predicted_new)
table(new_obs$Species, predicted_new)