predict.cubist {Cubist}

Predict method for cubist fits

Description

Prediction using the parametric model are calculated using the method of Quinlan (1992). If neighbors is greater than zero, these predictions are adjusted by training set instances nearby using the approach of Quinlan (1993).

Usage

## S3 method for class 'cubist'
predict(object, newdata = NULL, neighbors = 0, ...)

Arguments

object

an object of class cubist

newdata

a data frame of predictors (in the same order as the original training data). Must have column names.

neighbors

an integer from 0 to 9: how many instances to use to correct the rule-based prediction?

...

other options to pass through the function (not currently used)

Details

Note that the predictions can fail for various reasons. For example, as shown in the examples, if the model uses a qualitative predictor and the prediction data has a new level of that predictor, the function will throw an error.

Value

a numeric vector is returned

Author(s)

R code by Max Kuhn, original C sources by R Quinlan and modifications be Steve Weston

References

Quinlan. Learning with continuous classes. Proceedings of the 5th Australian Joint Conference On Artificial Intelligence (1992) pp. 343-348

Quinlan. Combining instance-based and model-based learning. Proceedings of the Tenth International Conference on Machine Learning (1993) pp. 236-243

Quinlan. C4.5: Programs For Machine Learning (1993) Morgan Kaufmann Publishers Inc. San Francisco, CA

http://rulequest.com/cubist-info.html

See Also

cubist(), cubistControl(), summary.cubist(), predict.cubist(), dotplot.cubist()

Examples

library(mlbench)
data(BostonHousing)

## 1 committee and no instance-based correction, so just an M5 fit:
mod1 <- cubist(x = BostonHousing[, -14], y = BostonHousing$medv)
predict(mod1, BostonHousing[1:4, -14])

## now add instances
predict(mod1, BostonHousing[1:4, -14], neighbors = 5)

# Example error
iris_test <- iris
iris_test$Species <- as.character(iris_test$Species)

mod <- cubist(x = iris_test[1:99, 2:5],
              y = iris_test$Sepal.Length[1:99])

# predict(mod, iris_test[100:151, 2:5])
# Error:
# *** line 2 of `undefined.cases':
# bad value of 'virginica' for attribute 'Species'