Laurae (version 0.0.0.9001)

pred.Lextravagenza: Laurae's Extravagenza machine learning model prediction

Description

This function predicts from data using a trained Extravagenza machine learning model. This does not work on multiclass problems.

Usage

pred.Lextravagenza(model, data, nrounds = model$best_iter)

Arguments

model

Type: list from Lextravgenza. The trained model.

data

Type: xgb.DMatrix. The data to predict on.

nrounds

Type: integer. The number of boosting iterations to predict from. Defaults to model$best_iter, which is the best iteration reported during Lextravagenza boosting.

Value

A prediction vector.

Examples

## Not run: ------------------------------------
# library(Laurae)
# library(xgboost)
# data(agaricus.train, package='xgboost')
# data(agaricus.test, package='xgboost')
# dtrain <- xgb.DMatrix(agaricus.train$data[1:5000, ], label = agaricus.train$label[1:5000])
# dval <- xgb.DMatrix(agaricus.train$data[5001:6513, ], label = agaricus.train$label[5001:6513])
# dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
# Lex_model <- Lextravagenza(train = dtrain, # Train data
#                            valid = dval, # Validation data = depth tuner
#                            test = dtest, # Test data = early stopper
#                            maximize = FALSE, # Not maximizing RMSE
#                            personal_rounds = 50, # Boosting for 50 iterations
#                            personal_depth = 1:8, # Dynamic depth between 1 and 8
#                            personal_eta = 0.40, # Shrinkage of boosting to 0.40
#                            auto_stop = 5, # Early stopping of 5 iterations
#                            base_margin = 0.5, # Start with 0.5 probabilities
#                            seed = 0, # Random seed
#                            nthread = 1, # 1 thread for training
#                            eta = 0.40, # xgboost shrinkage of 0.40 (avoid fast overfit)
#                            booster = "gbtree", # train trees, can't work with GLM
#                            objective = "binary:logistic", # classification, binary
#                            eval_metric = "rmse" # RMSE metric to optimize
# )
# 
# str(Lex_model, max.level = 1) # Get list of the model structure
# 
# predictedValues <- pred.Lextravagenza(Lex_model, dtest, nrounds = Lex_model$best_iter)
# all.equal(sqrt(mean((predictedValues - agaricus.test$label)^2)),
#           Lex_model$test[Lex_model$best_iter])
# 
## ---------------------------------------------