Type:	Package
Title:	Fast Machine Learning Model Training and Evaluation
Version:	0.6.1
Description:	Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code by providing comprehensive data preprocessing and support for a wide range of algorithms with hyperparameter tuning. It offers performance metrics and visualization tools to facilitate efficient and effective machine learning workflows.
Encoding:	UTF-8
License:	MIT + file LICENSE
URL:	https://github.com/selcukorkmaz/fastml
BugReports:	https://github.com/selcukorkmaz/fastml/issues
LazyData:	true
Imports:	recipes, dplyr, ggplot2, reshape2, rsample, parsnip, tune, workflows, yardstick, tibble, rlang, dials, RColorBrewer, baguette, bonsai, discrim, doFuture, finetune, future, plsmod, probably, viridisLite, DALEX, magrittr, patchwork, pROC, janitor, stringr, DT, GGally, UpSetR, VIM, broom, dbscan, ggpubr, gridExtra, htmlwidgets, kableExtra, moments, naniar, plotly, scales, skimr, tidyr, knitr, rmarkdown, purrr, mice, missForest
Suggests:	testthat (≥ 3.0.0), C50, glmnet, xgboost, ranger, crayon, kernlab, klaR, kknn, keras, lightgbm, rstanarm, mixOmics,
RoxygenNote:	7.3.2
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-06-10 22:04:39 UTC; selcukkorkmaz
Author:	Selcuk Korkmaz [aut, cre], Dincer Goksuluk [aut], Eda Karaismailoglu [aut]
Maintainer:	Selcuk Korkmaz <selcukorkmaz@gmail.com>
Depends:	R (≥ 3.5.0)
Repository:	CRAN
Date/Publication:	2025-06-10 22:50:02 UTC

Get Available Methods

Description

Returns a character vector of algorithm names available for either classification or regression tasks.

Usage

availableMethods(type = c("classification", "regression"), ...)

Arguments

Details

Depending on the specified type, the function returns a different set of algorithm names:

• For "classification", it returns algorithms such as "logistic_reg", "multinom_reg", "decision_tree", "C5_rules", "rand_forest", "xgboost", "lightgbm", "svm_linear", "svm_rbf", "nearest_neighbor", "naive_Bayes", "mlp", "discrim_linear", "discrim_quad", and "bag_tree".

• For "regression", it returns algorithms such as "linear_reg", "ridge_regression", "lasso_regression", "elastic_net", "decision_tree", "rand_forest", "xgboost", "lightgbm", "svm_linear", "svm_rbf", "nearest_neighbor", "mlp", "pls", and "bayes_glm".

Value

A character vector containing the names of the available algorithms for the specified task type.

Evaluate Models Function

Description

Evaluates the trained models on the test data and computes performance metrics.

Usage

evaluate_models(
  models,
  train_data,
  test_data,
  label,
  task,
  metric = NULL,
  event_class
)

Arguments

Value

A list with two elements:

performance

A named list of performance metric tibbles for each model.

predictions

A named list of data frames with columns including truth, predictions, and probabilities per model.

FastExplain the fastml (DALEX + SHAP + Permutation-based VI)

Description

Provides model explainability using DALEX. This function:

• Creates a DALEX explainer.

• Computes permutation-based variable importance with boxplots showing variability, displays the table and plot.

• Computes partial dependence-like model profiles if 'features' are provided.

• Computes Shapley values (SHAP) for a sample of the training observations, displays the SHAP table, and plots a summary bar chart of \text{mean}(\vert \text{SHAP value} \vert) per feature. For classification, it shows separate bars for each class.

Usage

fastexplain(
  object,
  method = "dalex",
  features = NULL,
  grid_size = 20,
  shap_sample = 5,
  vi_iterations = 10,
  seed = 123,
  loss_function = NULL,
  ...
)

Arguments

Details

1. Custom number of permutations for VI (vi_iterations):

   You can now specify how many permutations (B) to use for permutation-based variable importance. More permutations yield more stable estimates but take longer.

2. Better error messages and checks:

   Improved checks and messages if certain packages or conditions are not met.

3. Loss Function:

   A loss_function argument has been added to let you pick a different performance measure (e.g., loss_cross_entropy for classification, loss_root_mean_square for regression).

4. Parallelization Suggestion:

Value

Prints DALEX explanations: variable importance table & plot, model profiles (if any), and SHAP table & summary plot.

Explore and Summarize a Dataset Quickly

Description

fastexplore provides a fast and comprehensive exploratory data analysis (EDA) workflow. It automatically detects variable types, checks for missing and duplicated data, suggests potential ID columns, and provides a variety of plots (histograms, boxplots, scatterplots, correlation heatmaps, etc.). It also includes optional outlier detection, normality testing, and feature engineering.

Usage

fastexplore(
  data,
  label = NULL,
  visualize = c("histogram", "boxplot", "barplot", "heatmap", "scatterplot"),
  save_results = TRUE,
  output_dir = NULL,
  sample_size = NULL,
  interactive = FALSE,
  corr_threshold = 0.9,
  auto_convert_numeric = TRUE,
  visualize_missing = TRUE,
  imputation_suggestions = FALSE,
  report_duplicate_details = TRUE,
  detect_near_duplicates = TRUE,
  auto_convert_dates = FALSE,
  feature_engineering = FALSE,
  outlier_method = c("iqr", "zscore", "dbscan", "lof"),
  run_distribution_checks = TRUE,
  normality_tests = c("shapiro"),
  pairwise_matrix = TRUE,
  max_scatter_cols = 5,
  grouped_plots = TRUE,
  use_upset_missing = TRUE
)

Arguments

Details

This function automates many steps of EDA:

1. Automatically detects numeric vs. categorical variables.

2. Auto-converts columns that look numeric (and optionally date-like).

3. Summarizes data structure, missingness, duplication, and potential ID columns.

4. Computes correlation matrix and flags highly correlated pairs.

5. (Optional) Outlier detection using IQR, Z-score, DBSCAN, or LOF methods.

6. (Optional) Normality tests on numeric columns.

7. Saves all results and an R Markdown report if save_results = TRUE.

Value

A (silent) list containing:

• data_overview - A basic overview (head, unique values, skim summary).

• summary_stats - Summary statistics for numeric columns.

• freq_tables - Frequency tables for factor columns.

• missing_data - Missing data overview (count, percentage).

• duplicated_rows - Count of duplicated rows.

• class_imbalance - Class distribution if label is provided and is categorical.

• correlation_matrix - The correlation matrix for numeric variables.

• zero_variance_cols - Columns with near-zero variance.

• potential_id_cols - Columns with unique values in every row.

• date_time_cols - Columns recognized as date/time.

• high_corr_pairs - Pairs of variables with correlation above corr_threshold.

• outlier_method - The chosen method for outlier detection.

• outlier_summary - Outlier proportions or metrics (if computed).

If save_results = TRUE, additional side effects include saving figures, a correlation heatmap, and an R Markdown report in the specified directory.

Fast Machine Learning Function

Description

Trains and evaluates multiple classification or regression models automatically detecting the task based on the target variable type.

Usage

fastml(
  data = NULL,
  train_data = NULL,
  test_data = NULL,
  label,
  algorithms = "all",
  task = "auto",
  test_size = 0.2,
  resampling_method = "cv",
  folds = ifelse(grepl("cv", resampling_method), 10, 25),
  repeats = ifelse(resampling_method == "repeatedcv", 1, NA),
  event_class = "first",
  exclude = NULL,
  recipe = NULL,
  tune_params = NULL,
  metric = NULL,
  algorithm_engines = NULL,
  n_cores = 1,
  stratify = TRUE,
  impute_method = "error",
  impute_custom_function = NULL,
  encode_categoricals = TRUE,
  scaling_methods = c("center", "scale"),
  summaryFunction = NULL,
  use_default_tuning = FALSE,
  tuning_strategy = "grid",
  tuning_iterations = 10,
  early_stopping = FALSE,
  adaptive = FALSE,
  learning_curve = FALSE,
  seed = 123
)

Arguments

Details

Fast Machine Learning Function

Trains and evaluates multiple classification or regression models. The function automatically detects the task based on the target variable type and can perform advanced hyperparameter tuning using various tuning strategies.

Value

An object of class fastml containing the best model, performance metrics, and other information.

Examples

# Example 1: Using the iris dataset for binary classification (excluding 'setosa')
data(iris)
iris <- iris[iris$Species != "setosa", ]  # Binary classification
iris$Species <- factor(iris$Species)

# Train models
model <- fastml(
  data = iris,
  label = "Species",
  algorithms = c("rand_forest", "xgboost", "svm_rbf"), algorithm_engines = c(
  list(rand_forest = c("ranger","aorsf", "partykit", "randomForest")))
)

# View model summary
summary(model)


  

Flatten and Rename Models

Description

Flattens a nested list of models and renames the elements by combining the outer and inner list names.

Usage

flatten_and_rename_models(models)

Arguments

Details

The function iterates over each element of the outer list. For each element, if it is a list with names, the function concatenates the outer list name and the inner names using paste0 and setNames. If an element is not a list or does not have names, it is included in the result without modification.

Value

A flattened list with each element renamed according to its original outer and inner list names.

Framingham Heart Study Data

Description

This dataset is derived from the Framingham Heart Study and contains various clinical and demographic variables used to predict coronary heart disease risk over a ten-year period.

Format

male

Integer indicator for male sex.

age

Participant age in years.

education

Education level.

currentSmoker

Whether the participant currently smokes.

cigsPerDay

Number of cigarettes smoked per day.

BPMeds

Whether blood pressure medication is used.

prevalentStroke

History of stroke at baseline.

prevalentHyp

History of hypertension at baseline.

diabetes

Diabetes diagnosis.

totChol

Total cholesterol.

sysBP

Systolic blood pressure.

diaBP

Diastolic blood pressure.

BMI

Body mass index.

heartRate

Heart rate.

glucose

Glucose level.

TenYearCHD

Ten year risk of coronary heart disease.

Get Best Model Indices by Metric and Group

Description

Identifies and returns the indices of rows in a data frame where the specified metric reaches the overall maximum within groups defined by one or more columns.

Usage

get_best_model_idx(df, metric, group_cols = c("Model", "Engine"))

Arguments

Details

The function converts the metric values to numeric and creates a combined grouping factor using the specified group_cols. It then computes the maximum metric value within each group and determines the overall best metric value across the entire data frame. Finally, it returns the indices of rows belonging to groups that achieve this overall maximum.

Value

A numeric vector of row indices in df corresponding to groups whose maximum metric equals the overall best metric value.

Get Best Model Names

Description

Extracts and returns the best engine names from a named list of model workflows.

Usage

get_best_model_names(models)

Arguments

Details

For each algorithm, the function extracts the engine names from the model workflows using tune::extract_fit_parsnip. It then chooses "randomForest" if it is available; otherwise, it selects the first non-NA engine. If no engine names can be extracted for an algorithm, NA_character_ is returned.

Value

A named character vector. The names of the vector correspond to the algorithm names, and the values represent the chosen best engine name for that algorithm.

Get Best Workflows

Description

Extracts the best workflows from a nested list of model workflows based on the provided best model names.

Usage

get_best_workflows(models, best_model_name)

Arguments

Details

The function iterates over each element in best_model_name and attempts to extract the corresponding workflow from models using the specified engine. If the workflow for an algorithm-engine pair is not found, a warning is issued and NULL is returned for that entry.

Value

A named list of workflows corresponding to the best engine for each algorithm. Each list element is named in the format "algorithm (engine)".

Get Default Engine

Description

Returns the default engine corresponding to the specified algorithm.

Usage

get_default_engine(algo)

Arguments

Details

The function uses a switch statement to select the default engine based on the given algorithm. If the provided algorithm does not have a defined default engine, the function terminates with an error.

Value

A character string containing the default engine name associated with the provided algorithm.

Get Default Parameters for an Algorithm

Description

Returns a list of default tuning parameters for the specified algorithm based on the task type, number of predictors, and engine.

Usage

get_default_params(algo, task, num_predictors = NULL, engine = NULL)

Arguments

Details

The function employs a switch statement to select and return a list of default parameters tailored for the given algorithm, task, and engine. The defaults vary by algorithm and, in some cases, by engine. For example:

• For "rand_forest", if engine is not provided, it defaults to "ranger". The parameters such as mtry, trees, and min_n are computed based on the task and the number of predictors.

• For "C5_rules", the defaults include trees, min_n, and sample_size.

• For "xgboost" and "lightgbm", default values are provided for parameters like tree depth, learning rate, and sample size.

• For "logistic_reg" and "multinom_reg", the function returns defaults for regularization parameters (penalty and mixture) that vary with the specified engine.

• For "decision_tree", the parameters (such as tree_depth, min_n, and cost_complexity) are set based on the engine (e.g., "rpart", "C5.0", "partykit", "spark").

• Other algorithms, including "svm_linear", "svm_rbf", "nearest_neighbor", "naive_Bayes", "mlp", "deep_learning", "elastic_net", "bayes_glm", "pls", "linear_reg", "ridge_regression", and "lasso_regression", have their respective default parameter lists.

Value

A list of default parameter settings for the specified algorithm. If the algorithm is not recognized, the function returns NULL.

Get Default Tuning Parameters

Description

Returns a list of default tuning parameter ranges for a specified algorithm based on the provided training data, outcome label, and engine.

Usage

get_default_tune_params(algo, train_data, label, engine)

Arguments

Details

The function first determines the number of predictors by removing the outcome variable (specified by label) from train_data. It then uses a switch statement to select a list of default tuning parameter ranges tailored for the specified algorithm and engine. The tuning ranges have been adjusted for efficiency and may include parameters such as mtry, trees, min_n, and others depending on the algorithm.

Value

A list of tuning parameter ranges for the specified algorithm. If no tuning parameters are defined for the given algorithm, the function returns NULL.

Get Engine Names from Model Workflows

Description

Extracts and returns a list of unique engine names from a list of model workflows.

Usage

get_engine_names(models)

Arguments

Details

The function applies tune::extract_fit_parsnip to each model workflow to extract the fitted model object. It then retrieves the engine name from the model specification (spec$engine). If the extraction fails, NA_character_ is returned for that workflow. Finally, the function removes any duplicate engine names using unique.

Value

A list of character vectors. Each vector contains the unique engine names extracted from the corresponding element of models.

Get Model Engine Names

Description

Extracts and returns a named vector mapping algorithm names to engine names from a nested list of model workflows.

Usage

get_model_engine_names(models)

Arguments

Details

The function iterates over a nested list of model workflows and, for each workflow, attempts to extract the fitted model object using tune::extract_fit_parsnip. If successful, it retrieves the algorithm name from the first element of the class attribute of the model specification and the engine name from the specification. The results are combined into a named vector.

Value

A named character vector where the names correspond to algorithm names (e.g., "rand_forest", "logistic_reg") and the values correspond to the associated engine names (e.g., "ranger", "glm").

Load Model Function

Description

Loads a trained model object from a file.

Usage

load_model(filepath)

Arguments

Value

An object of class fastml.

Plot Methods for fastml Objects

Description

plot.fastml produces visual diagnostics for a trained fastml object.

Usage

## S3 method for class 'fastml'
plot(
  x,
  algorithm = "best",
  type = c("all", "bar", "roc", "confusion", "calibration", "residual"),
  ...
)

Arguments

Details

When type = "all", plot.fastml will produce a bar plot of metrics, ROC curves (classification), confusion matrix, calibration plot, and residual diagnostics (regression). If you specify a subset of types, only those will be drawn.

Examples

  ## Create a binary classification dataset from iris
  data(iris)
  iris <- iris[iris$Species != "setosa",]
  iris$Species <- factor(iris$Species)

  ## Fit fastml model on binary classification task
  model <- fastml(data = iris, label = "Species", algorithms = c("rand_forest", "svm_rbf"))

  ## 1. Plot all available diagnostics
  plot(model, type = "all")

  ## 2. Bar plot of performance metrics
  plot(model, type = "bar")

  ## 3. ROC curves (only for classification models)
  plot(model, type = "roc")

  ## 4. Calibration plot (requires 'probably' package)
  plot(model, type = "calibration")

  ## 5. ROC curves for specific algorithm(s) only
  plot(model, type = "roc", algorithm = "rand_forest")

  ## 6. Residual diagnostics (only available for regression tasks)
  model <- fastml(data = mtcars, label = "mpg", algorithms = c("linear_reg", "xgboost"))
  plot(model, type = "residual")

Predict method for fastml objects

Description

Generates predictions from a trained 'fastml' object on new data. Supports both single-model and multi-model workflows, and handles classification and regression tasks with optional post-processing and verbosity.

Usage

## S3 method for class 'fastml'
predict(
  object,
  newdata,
  type = "auto",
  model_name = NULL,
  verbose = FALSE,
  postprocess_fn = NULL,
  ...
)

Arguments

Value

A vector of predictions, or a named list of predictions (if multiple models are used). If 'postprocess_fn' is supplied, its output will be returned instead.

Examples

## Not run: 
  set.seed(123)
  model <- fastml(iris, label = "Species")
  test_data <- iris[sample(1:150, 20),-5]

  ## Best model(s) predictions
  preds <- predict(model, newdata = test_data)

  ## Predicted class probabilities using best model(s)
  probs <- predict(model, newdata = test_data, type = "prob")

  ## Prediction from a specific model by name
  single_model_preds <- predict(model, newdata = test_data, model_name = "rand_forest (ranger)")


## End(Not run)

Process Model and Compute Performance Metrics

Description

Finalizes a tuning result or utilizes an already fitted workflow to generate predictions on test data and compute performance metrics.

Usage

process_model(
  model_obj,
  model_id,
  task,
  test_data,
  label,
  event_class,
  engine,
  train_data,
  metric
)

Arguments

Details

The function first checks if model_obj is a tuning result. If so, it attempts to:

• Select the best tuning parameters using tune::select_best (note that the metric used for selection should be defined in the calling environment).

• Extract the model specification and preprocessor from model_obj using workflows::pull_workflow_spec and workflows::pull_workflow_preprocessor, respectively.

• Finalize the model specification with the selected parameters via tune::finalize_model.

• Rebuild the workflow using workflows::workflow, workflows::add_recipe, and workflows::add_model, and fit the finalized workflow with parsnip::fit on the supplied train_data.

If model_obj is already a fitted workflow, it is used directly.

For classification tasks, the function makes class predictions (and probability predictions if engine is not "LiblineaR") and computes performance metrics using functions from the yardstick package. In binary classification, the positive class is determined based on the event_class argument and ROC AUC is computed accordingly. For multiclass classification, macro-averaged metrics and ROC AUC (using weighted estimates) are calculated.

For regression tasks, the function predicts outcomes and computes regression metrics (RMSE, R-squared, and MAE).

If the number of predictions does not match the number of rows in test_data, the function stops with an informative error message regarding missing values and imputation options.

Value

A list with two components:

performance

A data frame of performance metrics. For classification tasks, metrics include accuracy, kappa, sensitivity, specificity, precision, F-measure, and ROC AUC (when applicable). For regression tasks, metrics include RMSE, R-squared, and MAE.

predictions

A data frame containing the test data augmented with predicted classes and, when applicable, predicted probabilities.

Clean Column Names or Character Vectors by Removing Special Characters

Description

This function can operate on either a data frame or a character vector:

• Data frame: Detects columns whose names contain any character that is not a letter, number, or underscore, removes colons, replaces slashes with underscores, and spaces with underscores.

• Character vector: Applies the same cleaning rules to every element of the vector.

Usage

sanitize(x)

Arguments

Value

• If x is a data frame: returns a data frame with cleaned column names.

• If x is a character vector: returns a character vector with cleaned elements.

Save Model Function

Description

Saves the trained model object to a file.

Usage

save.fastml(model, filepath)

Arguments

Value

No return value, called for its side effect of saving the model object to a file.

Summary Function for fastml (Using yardstick for ROC Curves)

Description

Summarizes the results of machine learning models trained using the 'fastml' package. Depending on the task type (classification or regression), it provides customized output such as performance metrics, best hyperparameter settings, and confusion matrices. It is designed to be informative and readable, helping users quickly interpret model results.

Usage

## S3 method for class 'fastml'
summary(
  object,
  algorithm = "best",
  type = c("all", "metrics", "params", "conf_mat"),
  sort_metric = NULL,
  ...
)

Arguments

Details

For classification tasks, the summary includes metrics such as Accuracy, F1 Score, Kappa, Precision, ROC AUC, Sensitivity, and Specificity. A confusion matrix is also provided for the best model(s). For regression tasks, the summary reports RMSE, R-squared, and MAE.

Users can control the type of output with the 'type' argument: 'metrics' displays model performance metrics. 'params' shows the best hyperparameter settings. 'conf_mat' prints confusion matrices (only for classification). 'all' includes all of the above.

If multiple algorithms are trained, the summary highlights the best model based on the optimized metric.

Value

Prints summary of fastml models.

Train Specified Machine Learning Algorithms on the Training Data

Description

Trains specified machine learning algorithms on the preprocessed training data.

Usage

train_models(
  train_data,
  label,
  task,
  algorithms,
  resampling_method,
  folds,
  repeats,
  tune_params,
  metric,
  summaryFunction = NULL,
  seed = 123,
  recipe,
  use_default_tuning = FALSE,
  tuning_strategy = "grid",
  tuning_iterations = 10,
  early_stopping = FALSE,
  adaptive = FALSE,
  algorithm_engines = NULL
)

Arguments

Value

A list of trained model objects.