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

In Laurae2/Laurae: Advanced High Performance Data Science Toolbox for R

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])


## End(Not run)

Laurae2/Laurae documentation built on May 8, 2019, 7:59 p.m.