Function reference • mlr3

Building Blocks

Task: Task Class

Learner: Learner Class

Measure: Measure Class

Resampling: Resampling Class

Prediction: Abstract Prediction Object

Data Backends

DataBackend: DataBackend

DataBackendDataTable: DataBackend for data.table

DataBackendMatrix: DataBackend for Matrix

as_data_backend(): Create a Data Backend

Classification

TaskClassif: Classification Task

as_task_classif(): Convert to a Classification Task

LearnerClassif: Classification Learner

MeasureClassif: Classification Measure

PredictionClassif: Prediction Object for Classification

Regression

TaskRegr: Regression Task

as_task_regr(): Convert to a Regression Task

LearnerRegr: Regression Learner

MeasureRegr: Regression Measure

PredictionRegr: Prediction Object for Regression

Tasks

mlr_tasks: Dictionary of Tasks

mlr_tasks_boston_housing: Boston Housing Regression Task

mlr_tasks_breast_cancer: Wisconsin Breast Cancer Classification Task

mlr_tasks_german_credit: German Credit Classification Task

mlr_tasks_iris: Iris Classification Task

mlr_tasks_mtcars: Motor Trend Regression Task

mlr_tasks_penguins: Palmer Penguins Data Set

mlr_tasks_pima: Pima Indian Diabetes Classification Task

mlr_tasks_sonar: Sonar Classification Task

mlr_tasks_spam: Spam Classification Task

mlr_tasks_wine: Wine Classification Task

mlr_tasks_zoo: Zoo Classification Task

as_task() as_tasks(): Convert to a Task

convert_task(): Convert a Task from One Type to Another

Task Generators

TaskGenerator: TaskGenerator Class

mlr_task_generators: Dictionary of Task Generators

mlr_task_generators_2dnormals TaskGenerator2DNormals: 2D Normals Classification Task Generator

mlr_task_generators_cassini TaskGeneratorCassini: Cassini Classification Task Generator

mlr_task_generators_circle TaskGeneratorCircle: Circle Classification Task Generator

mlr_task_generators_friedman1 TaskGeneratorFriedman1: Friedman1 Regression Task Generator

mlr_task_generators_moons TaskGeneratorMoons: Moons Classification Task Generator

mlr_task_generators_simplex TaskGeneratorSimplex: Simplex Classification Task Generator

mlr_task_generators_smiley TaskGeneratorSmiley: Smiley Classification Task Generator

mlr_task_generators_spirals TaskGeneratorSpirals: Spiral Classification Task Generator

mlr_task_generators_xor TaskGeneratorXor: XOR Classification Task Generator

Learners

mlr_learners: Dictionary of Learners

mlr_learners_classif.debug LearnerClassifDebug: Classification Learner for Debugging

mlr_learners_classif.featureless LearnerClassifFeatureless: Featureless Classification Learner

mlr_learners_classif.rpart LearnerClassifRpart: Classification Tree Learner

mlr_learners_regr.debug LearnerRegrDebug: Regression Learner for Debugging

mlr_learners_regr.featureless LearnerRegrFeatureless: Featureless Regression Learner

mlr_learners_regr.rpart LearnerRegrRpart: Regression Tree Learner

as_learner() as_learners(): Convert to a Learner

HotstartStack: Stack for Hot Start Learners

Measures

mlr_measures: Dictionary of Performance Measures

mlr_measures_aic MeasureAIC: Akaike Information Criterion Measure

mlr_measures_bic MeasureBIC: Bayesian Information Criterion Measure

mlr_measures_classif.acc: Classification Accuracy

mlr_measures_classif.auc: Area Under the ROC Curve

mlr_measures_classif.bacc: Balanced Accuracy

mlr_measures_classif.bbrier: Binary Brier Score

mlr_measures_classif.ce: Classification Error

mlr_measures_classif.costs MeasureClassifCosts: Cost-sensitive Classification Measure

mlr_measures_classif.dor: Diagnostic Odds Ratio

mlr_measures_classif.fbeta: F-beta Score

mlr_measures_classif.fdr: False Discovery Rate

mlr_measures_classif.fn: False Negatives

mlr_measures_classif.fnr: False Negative Rate

mlr_measures_classif.fomr: False Omission Rate

mlr_measures_classif.fp: False Positives

mlr_measures_classif.fpr: False Positive Rate

mlr_measures_classif.logloss: Log Loss

mlr_measures_classif.mauc_au1p: Multiclass AUC Scores

mlr_measures_classif.mauc_au1u: Multiclass AUC Scores

mlr_measures_classif.mauc_aunp: Multiclass AUC Scores

mlr_measures_classif.mauc_aunu: Multiclass AUC Scores

mlr_measures_classif.mbrier: Multiclass Brier Score

mlr_measures_classif.mcc: Matthews Correlation Coefficient

mlr_measures_classif.npv: Negative Predictive Value

mlr_measures_classif.ppv: Positive Predictive Value

mlr_measures_classif.prauc: Area Under the Precision-Recall Curve

mlr_measures_classif.precision: Positive Predictive Value

mlr_measures_classif.recall: True Positive Rate

mlr_measures_classif.sensitivity: True Positive Rate

mlr_measures_classif.specificity: True Negative Rate

mlr_measures_classif.tn: True Negatives

mlr_measures_classif.tnr: True Negative Rate

mlr_measures_classif.tp: True Positives

mlr_measures_classif.tpr: True Positive Rate

mlr_measures_debug_classif MeasureDebugClassif: Debug Measure for Classification

mlr_measures_elapsed_time MeasureElapsedTime mlr_measures_time_train mlr_measures_time_predict mlr_measures_time_both: Elapsed Time Measure

mlr_measures_oob_error MeasureOOBError: Out-of-bag Error Measure

mlr_measures_regr.bias: Bias

mlr_measures_regr.ktau: Kendall's tau

mlr_measures_regr.mae: Mean Absolute Error

mlr_measures_regr.mape: Mean Absolute Percent Error

mlr_measures_regr.maxae: Max Absolute Error

mlr_measures_regr.medae: Median Absolute Error

mlr_measures_regr.medse: Median Squared Error

mlr_measures_regr.mse: Mean Squared Error

mlr_measures_regr.msle: Mean Squared Log Error

mlr_measures_regr.pbias: Percent Bias

mlr_measures_regr.rae: Relative Absolute Error

mlr_measures_regr.rmse: Root Mean Squared Error

mlr_measures_regr.rmsle: Root Mean Squared Log Error

mlr_measures_regr.rrse: Root Relative Squared Error

mlr_measures_regr.rse: Relative Squared Error

mlr_measures_regr.rsq: R Squared

mlr_measures_regr.sae: Sum of Absolute Errors

mlr_measures_regr.smape: Symmetric Mean Absolute Percent Error

mlr_measures_regr.srho: Spearman's rho

mlr_measures_regr.sse: Sum of Squared Errors

mlr_measures_selected_features MeasureSelectedFeatures: Selected Features Measure

mlr_measures_sim.jaccard: Jaccard Similarity Index

mlr_measures_sim.phi: Phi Coefficient Similarity

default_measures(): Get the Default Measure

as_measure as_measure.NULL as_measure.Measure as_measures as_measures.default as_measures.NULL as_measures.list: Convert to a Measure

Resampling Strategies

mlr_resamplings: Dictionary of Resampling Strategies

mlr_resamplings_bootstrap ResamplingBootstrap: Bootstrap Resampling

mlr_resamplings_custom ResamplingCustom: Custom Resampling

mlr_resamplings_custom_cv ResamplingCustomCV: Custom Cross-Validation

mlr_resamplings_cv ResamplingCV: Cross-Validation Resampling

mlr_resamplings_holdout ResamplingHoldout: Holdout Resampling

mlr_resamplings_insample ResamplingInsample: Insample Resampling

mlr_resamplings_loo ResamplingLOO: Leave-One-Out Cross-Validation

mlr_resamplings_repeated_cv ResamplingRepeatedCV: Repeated Cross-Validation Resampling

mlr_resamplings_subsampling ResamplingSubsampling: Subsampling Resampling

as_resampling() as_resamplings(): Convert to a Resampling

Resample

resample(): Resample a Learner on a Task

partition(): Manually Partition into Training and Test Set

ResampleResult: Container for Results of resample()

as_result_data(): Convert to ResultData

as_resample_result(): Convert to ResampleResult

Benchmarking

benchmark(): Benchmark Multiple Learners on Multiple Tasks

benchmark_grid(): Generate a Benchmark Grid Design

BenchmarkResult: Container for Benchmarking Results

as_result_data(): Convert to ResultData

as_benchmark_result(): Convert to BenchmarkResult

Converters

as_benchmark_result(): Convert to BenchmarkResult

as_data_backend(): Create a Data Backend

as_learner() as_learners(): Convert to a Learner

as_measure as_measure.NULL as_measure.Measure as_measures as_measures.default as_measures.NULL as_measures.list: Convert to a Measure

as_prediction() as_predictions(): Convert to a Prediction

as_prediction_classif(): Convert to a Classification Prediction

as_prediction_data(): PredictionData

as_prediction_regr(): Convert to a Regression Prediction

as_resample_result(): Convert to ResampleResult

as_resampling() as_resamplings(): Convert to a Resampling

as_result_data(): Convert to ResultData

as_task() as_tasks(): Convert to a Task

as_task_classif(): Convert to a Classification Task

as_task_regr(): Convert to a Regression Task

as_task_unsupervised() as_tasks_unsupervised(): Convert to an Unsupervised Task

Syntactic Sugar and Reflections

install_pkgs() extract_pkgs(): Install (Missing) Packages

tsk() tsks() tgen() tgens() lrn() lrns() rsmp() rsmps() msr() msrs(): Syntactic Sugar for Object Construction

mlr_reflections: Reflections for mlr3

set_threads(): Set the Number of Threads

Internal Objects and Functions

MeasureSimilarity: Similarity Measure

assert_backend() assert_task() assert_tasks() assert_learner() assert_learners() assert_learnable() assert_predictable() assert_measure() assert_measures() assert_resampling() assert_resamplings() assert_prediction() assert_resample_result() assert_benchmark_result() assert_row_ids(): Assertion for mlr3 Objects

ResultData: ResultData

check_prediction_data() is_missing_prediction_data() filter_prediction_data() c(<PredictionDataClassif>) c(<PredictionDataRegr>): Convert to PredictionData

predict(<Learner>): Predict Method for Learners

Package

mlr3 mlr3-package: mlr3: Machine Learning in R - Next Generation