Description Usage Arguments Details Value Author(s) Examples
This function finds the optimal parameters of an algorithm using random search
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y |
a numeric vector |
tune_iters |
a number |
resampling_method |
one of 'bootstrap', 'train_test_split', 'cross_validation' |
ALGORITHM |
a list of parameters |
grid_params |
a grid of parameters in form of a list |
DATA |
a list including the data |
Args |
a list with further arguments of the function |
regression |
a boolean (TRUE, FALSE) |
re_run_params |
a boolean (TRUE, FALSE) |
UNLABELED_TEST_DATA |
either NULL or a data.frame ( matrix ) with the same number of columns as the initial train data |
... |
ellipsis to allow additional parameters |
This function takes a number of arguments (including a grid of parameters) of an algorithm and using random search it returns a list of predictions and parameters for the chosen resampling method.
a list of lists
Lampros Mouselimis
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#..........................
# MULTICLASS CLASSIFICATION
#..........................
library(kknn)
data(glass)
str(glass)
X = glass[, -c(1, dim(glass)[2])]
y1 = glass[, dim(glass)[2]]
form <- as.formula(paste('Type ~', paste(names(X),collapse = '+')))
y1 = c(1:length(unique(y1)))[ match(y1, sort(unique(y1))) ] # labels should begin from 1:Inf
ALL_DATA = glass
ALL_DATA$Type = as.factor(y1)
#........................
# randomForest classifier
#........................
wrap_grid_args3 = list(ntree = seq(30, 50, 5), mtry = c(2:3), nodesize = seq(5, 15, 5))
res_rf = random_search_resample(as.factor(y1), tune_iters = 15,
resampling_method = list(method = 'cross_validation',
repeats = NULL,
sample_rate = NULL,
folds = 5),
ALGORITHM = list(package = require(randomForest),
algorithm = randomForest),
grid_params = wrap_grid_args3,
DATA = list(x = X, y = as.factor(y1)),
Args = NULL,
regression = FALSE, re_run_params = FALSE)
#............
# Logit boost
#............
#...........................
# RWeka::WOW("LogitBoost") : gives info for the parameters of the RWeka control list
#...........................
lb_lst = list(control = RWeka::Weka_control(H = c(1.0, 0.5),
I = seq(10, 30, 5),
Q = c(TRUE, FALSE),
O = 4))
res_log_boost = random_search_resample(as.factor(y1), tune_iters = 15,
resampling_method = list(method = 'cross_validation',
repeats = NULL,
sample_rate = NULL,
folds = 5),
ALGORITHM = list(package = require(RWeka),
algorithm = LogitBoost),
grid_params = lb_lst,
DATA = list(formula = form, data = ALL_DATA),
Args = NULL,
regression = FALSE, re_run_params = FALSE)
## End(Not run)
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