predict.adaboost {JOUSBoost} | R Documentation |
Makes a prediction on new data for a given fitted adaboost
model.
## S3 method for class 'adaboost' predict(object, X, type = c("response", "prob"), n_tree = NULL, ...)
object |
An object of class |
X |
A design matrix of predictors. |
type |
The type of prediction to return. If |
n_tree |
The number of trees to use in the prediction (by default, all them). |
... |
... |
Returns a vector of class predictions if type="response"
, or a
vector of class probabilities p(y=1|x) if type="prob"
.
Probabilities are estimated according to the formula:
p(y=1| x) = 1/(1 + exp(-2*f(x)))
where f(x) is the score function produced by AdaBoost. See Friedman (2000).
Friedman, J., Hastie, T. and Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion), Annals of Statistics 28: 337-307.
## Not run: # Generate data from the circle model set.seed(111) dat = circle_data(n = 500) train_index = sample(1:500, 400) ada = adaboost(dat$X[train_index,], dat$y[train_index], tree_depth = 2, n_rounds = 100, verbose = TRUE) # get class prediction yhat = predict(ada, dat$X[-train_index, ]) # get probability estimate phat = predict(ada, dat$X[-train_index, ], type="prob") ## End(Not run)