learn: Learning models from data
In Modeler: Classes and Methods for Training and Using Binary Prediction Models

Description Usage Arguments Details Value Author(s) See Also Examples

The learn function provides an abstraction that can be used to fit a binary classification model to a training data set.

1	learn(model, data, status, prune=keepAll)

`model`	An object of the `Modeler-class`
`data`	A matrix containing the training data, with rows as features and columns as samples to be classified.
`status`	A factor, with two levels, containing the known classification of the training data.
`prune`	A "pruning" funciton; that is, a funciton that takes two arguments (a data matrix and a class factor) and returns a logical vector indicating which features to retain.

Objects of the Modeler-class contain functions to learn models from training data to make predictions on new test data. These functions have to be prepared as pairs, since they have a shared opinion about how to record and use specific details about the parameters of the model. As a result, the learn function is implemented by:

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  learn <- function(model, data, status) {
    model@learn(data, status, model@params, model@predict)
  }

An object of the FittedModel-class.

Kevin R. Coombes <krc@silicovore.com>

See predict for how to make predictions on new test data from an object of the FittedModel-class.

# set up a generic RPART model
rpart.mod <- Modeler(learnRPART, predictRPART, minsplit=2, minbucket=1)

# simulate fake data
data <- matrix(rnorm(100*20), ncol=20)
status <- factor(rep(c("A", "B"), each=10))

# learn the specific RPART model
fm <- learn(rpart.mod, data, status)

# show the predicted results from the model on the trianing data
predict(fm)

# set up a nearest neighbor model
knn.mod <- Modeler(learnKNN, predictKNN, k=3)

# fit the 3NN model on the same data
fm3 <- learn(knn.mod, data, status)
# show its performance
predict(fm3)

Loading required package: ClassDiscovery
Loading required package: cluster
Loading required package: oompaBase
Loading required package: ClassComparison
   A B
1  1 0
2  1 0
3  1 0
4  1 0
5  1 0
6  1 0
7  1 0
8  1 0
9  1 0
10 1 0
11 0 1
12 0 1
13 0 1
14 0 1
15 0 1
16 0 1
17 0 1
18 0 1
19 0 1
20 0 1
 [1] A A A A A A A A B A B B A B B B B B B B
Levels: A B