LazyBoostingClassifier

Lazy Generic Boosting Classifier (AutoML Hold-out set validation)

LazyBoostingClassifier(
  verbose = 0,
  ignore_warnings = TRUE,
  custom_metric = NULL,
  predictions = FALSE,
  sort_by = "Accuracy",
  random_state = 42,
  estimators = "all",
  preprocess = FALSE,
  n_jobs = NULL
)

Arguments

verbose:

int, progress bar (yes = 1) or not (no = 0) (currently).

ignore_warnings:

bool, ignore warnings.

custom_metric:

function, custom metric.

predictions:

bool, return predictions.

sort_by:

str, sort by metric.

random_state:

int, random state.

estimators:

str, estimators to use. List of names for custom, or just 'all'.

preprocess:

bool, preprocess data or not.

n_jobs:

int, number of jobs.

Value

LazyBoostingClassifier object

Examples

library(mlsauce)
library(datasets)

data(iris)

X <- as.matrix(iris[, 1:4])
y <- as.integer(iris[, 5]) - 1L

n <- dim(X)[1]
p <- dim(X)[2]

set.seed(21341)
train_index <- sample(x = 1:n, size = floor(0.8*n), replace = TRUE)
test_index <- -train_index

X_train <- as.matrix(X[train_index, ])
y_train <- as.integer(y[train_index])
X_test <- as.matrix(X[test_index, ])
y_test <- as.integer(y[test_index])

obj <- LazyBoostingClassifier(verbose=0, ignore_warnings=TRUE,
                              custom_metric=NULL, preprocess=FALSE)

obj$fit(X_train, X_test, y_train, y_test)
#> [[1]]
#>                         Accuracy Balanced Accuracy ROC AUC  F1 Score Time Taken
#> RandomForestClassifier 0.9701493         0.9738462    <NA> 0.9701493 0.23140717
#> XGBClassifier          0.8507463         0.8676923    <NA> 0.8495071 0.06676912
#> 
#> [[2]]
#>                         Accuracy Balanced Accuracy ROC AUC  F1 Score Time Taken
#> RandomForestClassifier 0.9701493         0.9738462    <NA> 0.9701493 0.23140717
#> XGBClassifier          0.8507463         0.8676923    <NA> 0.8495071 0.06676912
#>