# predict.sbrl: PREDICT THE POSITIVE PROBABILITY FOR THE OBSERVATIONS In sbrl: Scalable Bayesian Rule Lists Model

## Description

Returns a list of probabilities.

## Usage

 ```1 2``` ```## S3 method for class 'sbrl' predict(object, tdata, ...) ```

## Arguments

 `object` sbrl model returned from the `sbrl` function. `tdata` test data `...` further arguments passed to or from other methods.

## Value

return a list containing 2 lists of probablities for the rule list, corresponding to probability being 0 and 1 for each observation. The two probabilities for each rule add up to 1, P(y=0 | rule r) + p(y=1 | rule r) = 1

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```# Let us use the titactoe dataset data(tictactoe) for (name in names(tictactoe)) {tictactoe[name] <- as.factor(tictactoe[,name])} # Train on two-thirds of the data b = round(2*nrow(tictactoe)/3, digit=0) data_train <- tictactoe[1:b, ] # Test on the remaining one third of the data data_test <- tictactoe[(b+1):nrow(tictactoe), ] # data_train, data_test are dataframes with factor columns # The class column is "label" # Run the sbrl algorithm on the training set sbrl_model <- sbrl(data_train, iters=20000, pos_sign="1", neg_sign="0", rule_minlen=1, rule_maxlen=3, minsupport_pos=0.10, minsupport_neg=0.10, lambda=10.0, eta=1.0, nchain=25) print(sbrl_model) # Make predictions on the test set yhat <- predict(sbrl_model, data_test) # yhat will be a list of predicted negative and positive probabilities for the test data. ```

sbrl documentation built on May 29, 2017, 2:47 p.m.