End-to-End Automated Machine Learning and Model Evaluation
Aggregate variable importance from multiple variable importance object...
Creates a valid data object from input data.
Conversion to familiarCollection object.
Conversion to familiarData object.
Conversion to familiarEnsemble object.
Extract model coefficients
Create randomised groups Creates randomised groups, e.g. for tests tha...
Data object
Internal function to test plausibility of provided class levels
Internal function to check whether feature columns are found in the da...
Internal function for checking consistency of the identifier columns
Internal checks on common plot input arguments
Internal function for checking if the outcome type fits well to the da...
Checks and sanitizes splitting variables for plotting.
Internal function to test plausibility of provided survival times.
Internal function for finalising generic data processing
Internal function for obtaining a default signature size parameter
Internal function for creating or retrieving iteration data
Internal imputation function for the outcome type.
Internal function for loading iteration data from the file system
Internal function for setting categorical features
Internal function for parsing settings related to model evaluation
Internal function for parsing settings related to the experimental set...
Internal function for parsing settings related to feature selection
Internal function for parsing file paths
Internal function for parsing settings that configure various aspects ...
Internal function for parsing settings related to hyperparameter optim...
Internal function for parsing settings required to parse the input dat...
Internal function for converting integer features
Internal function for parsing settings related to model development
Internal function for parsing settings related to preprocessing
Internal function for parsing settings related to the computational se...
Internal plotting function for permutation variable importance plots
Internal plotting function for univariate plots
Prepare familiarData objects for evaluation at runtime.
Internal function to check batch assignment to development and validat...
Internal check and update of settings related to data set parsing
Encapsulate path
Experiment data
Extract and export all data.
Extract and export ROC and Precision-Recall curves.
Extract and export calibration and goodness-of-fit tests.
Extract and export calibration information.
Extract and export confusion matrices.
Extract and export decision curve analysis data.
Extract and export feature expressions.
Extract and export mutual correlation between features.
Extract and export feature selection variable importance.
Extract and export model hyperparameters.
Extract and export individual conditional expectation data.
Extract and export metrics for model performance.
Extract and export model-based variable importance.
Extract and export partial dependence data.
Extract and export permutation variable importance.
Extract and export predicted values.
Extract and export sample risk group stratification and associated tes...
Extract and export cut-off values for risk group stratification.
Extract and export mutual correlation between features.
Extract and export univariate analysis data of features.
Internal function to extract area under the ROC curve information.
Internal function to extract calibration data.
Internal function to extract calibration info from data.
Internal function to extract the confusion matrix.
Internal function to create a familiarData object.
Internal function to extract decision curve analysis data.
Internal function to dispatch extraction functions.
Parse experimental design
Internal function to extract feature expressions.
Internal function to extract the feature distance table.
Internal function to extract feature selection variable importance.
Internal function to extract hyperparameters from models.
Internal function to extract data for individual conditional expectati...
Internal function to extract variable importance from models.
Internal function to extract performance metrics.
Internal function to extract permutation variable importance.
Internal function to extract predicted values from models.
Internal function to extract stratification data.
Internal function to extract risk stratification info from data.
Internal function to extract the sample distance table.
Internal function to extract data from a univariate analysis.
familiar: Fully Automated Machine Learning with Interpretable Analysis...
Collection of familiar data.
Dataset obtained after evaluating models on a dataset.
Data container for evaluation data.
Ensemble of familiar models.
Hyperparameter learner.
Model performance metric.
Familiar model.
Novelty detector.
Variable importance method object.
Feature information object.
Feature information parameters object.
Get outcome class labels
Get current name of datasets
Get current feature labels
Get current feature selection method name labels
Get current learner name labels
Get current risk group labels
Extract variable importance table.
Create an empty xml configuration file
Internal test for encapsulated_path
Internal test to see if an object is a waiver
Outcome information object.
Plot the precision-recall curve.
Plot the receiver operating characteristic curve.
Plot calibration figures.
Plot confusion matrix.
Plot decision curves.
Plot heatmaps for pairwise similarity between features.
Plot individual conditional expectation plots.
Plot Kaplan-Meier survival curves.
Plot model performance.
Plot partial dependence.
Plot permutation variable importance.
Plot heatmaps for pairwise similarity between features.
Plot univariate importance.
Plot variable importance scores of features during feature selection o...
Pre-compute data assignment
Pre-compute feature information
Pre-compute variable importance
Model predictions for familiar models and model ensembles
Rename outcome classes for plotting and export
Name datasets for plotting and export
Rename features for plotting and export
Rename feature selection methods for plotting and export
Rename learners for plotting and export
Set the name of a familiarData object.
Set the name of a familiarEnsemble object.
Set the name of a familiarModel object.
Rename risk groups for plotting and export
Model summaries
Perform end-to-end machine learning and data analysis
Familiar ggplot2 theme
Create models using end-to-end machine learning
Updates model directory path for ensemble objects.
Update familiar S4 objects to the most recent version.
Calculate variance-covariance matrix for a model
Variable importance table
Create a waiver object
Single unified interface for end-to-end modelling of regression, categorical and time-to-event (survival) outcomes. Models created using familiar are self-containing, and their use does not require additional information such as baseline survival, feature clustering, or feature transformation and normalisation parameters. Model performance, calibration, risk group stratification, (permutation) variable importance, individual conditional expectation, partial dependence, and more, are assessed automatically as part of the evaluation process and exported in tabular format and plotted, and may also be computed manually using export and plot functions. Where possible, metrics and values obtained during the evaluation process come with confidence intervals.
Useful links