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library(easyml) # https://github.com/CCS-Lab/easyml
# Load data
data("cocaine_dependence", package = "easyml")
# Settings
.n_samples <- 50
.n_divisions <- 50
.n_iterations <- 5
.n_core <- 1
# Analyze data
results <- easy_random_forest(cocaine_dependence, "diagnosis",
family = "binomial", exclude_variables = c("subject"),
categorical_variables = c("male"),
n_samples = .n_samples, n_divisions = .n_divisions,
n_iterations = .n_iterations, random_state = 12345, n_core = .n_core)
results$plot_variable_importances
results$plot_predictions_single_train_test_split_train
results$plot_predictions_single_train_test_split_test
results$plot_roc_single_train_test_split_train
results$plot_roc_single_train_test_split_test
results$plot_model_performance_train
results$plot_model_performance_test
# Analyze data
results <- easy_support_vector_machine(cocaine_dependence, "diagnosis",
family = "binomial", preprocess = preprocess_scale,
exclude_variables = c("subject"),
categorical_variables = c("male"),
n_samples = .n_samples, n_divisions = .n_divisions,
n_iterations = .n_iterations, random_state = 12345, n_core = .n_core)
results$plot_predictions_single_train_test_split_train
results$plot_predictions_single_train_test_split_test
results$plot_roc_single_train_test_split_train
results$plot_roc_single_train_test_split_test
results$plot_model_performance_train
results$plot_model_performance_test
# Analyze data
results <- easy_glmnet(cocaine_dependence, "diagnosis",
family = "binomial",
exclude_variables = c("subject"),
categorical_variables = c("male"),
n_samples = .n_samples, n_divisions = .n_divisions,
n_iterations = .n_iterations, random_state = 12345, n_core = .n_core,
model_args = list(alpha = 1, nlambda = 200))
results$plot_coefficients
results$plot_predictions_single_train_test_split_train
results$plot_predictions_single_train_test_split_test
results$plot_roc_single_train_test_split_train
results$plot_roc_single_train_test_split_test
results$plot_model_performance_train
results$plot_model_performance_test
glinternet_results <- easy_glinternet(cocaine_dependence, "diagnosis",
family = "binomial",
resample = resample_stratified_class_train_test_split,
preprocess = preprocess_scale,
exclude_variables = c("subject"),
categorical_variables = c("male"),
n_samples = .n_samples, n_divisions = .n_divisions,
n_iterations = .n_iterations, random_state = 12345, n_core = .n_core)
neural_network_results <- easy_neural_network(cocaine_dependence, "diagnosis",
family = "binomial", preprocess = preprocess_scale,
exclude_variables = c("subject"),
categorical_variables = c("male"),
n_samples = .n_samples, n_divisions = .n_divisions,
n_iterations = .n_iterations, random_state = 12345, n_core = .n_core,
model_args = list(size = c(40)))
library(darch)
deep_neural_network_results <- easy_deep_neural_network(cocaine_dependence, "diagnosis",
family = "binomial", preprocess = preprocess_scale,
exclude_variables = c("subject"),
categorical_variables = c("male"),
n_samples = .n_samples, n_divisions = .n_divisions,
n_iterations = .n_iterations, random_state = 12345, n_core = .n_core)
model_args <- list(size = 5, linout = TRUE, trace = FALSE)
b <- easy_avNNet(cocaine_dependence, "diagnosis",
family = "binomial",
preprocess = preprocess_scale,
exclude_variables = c("subject"),
categorical_variables = c("male"),
n_samples = 10, n_divisions = 10,
n_iterations = 10, random_state = 12345,
n_core = 1, model_args = model_args)
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