mlrCPO0.3.8 package

Apply a CPO to Data

Split a Pipeline into Its Constituents

Attach a CPO to a Learner

Clear Retrafo and Inverter Attributes

CPO Composition

Add 'covr' coverage to CPOs

Composable Preprocessing Operators

Apply a Function Element-Wise

Transform a Regression Target Variable

Convert All Features to Numerics

Caches the Result of CPO Transformations

cbind the Result of Multiple CPOs

Compine Rare Factors

Constructor for CPO Objects

Drop Constant or Near-Constant Features

Drop Constant or Near-Constant Features

CPO Dummy Encoder

Filter Features: anova.test

Filter Features: carscore

Filter Features: chi.squared

Filter Features by Thresholding Filter Values

Filter Features: gain.ratio

Filter Features: information.gain

Filter Features: kruskal.test

Filter Features: linear.correlation

Filter Features: mrmr

Filter Features: oneR

Filter Features: permutation.importance

Filter Features: rank.correlation

Filter Features: relief

Filter Features: cforest.importance

Filter Features: randomForest.importance

Filter Features: randomForestSRC.rfsrc

Filter Features: symmetrical.uncertainty

Filter Features: univariate.model.score

Filter Features: variance

Clean Up Factorial Features

Construct a CPO for ICA Preprocessing

Impact Encoding

Impact Encoding

Impute and Re-Impute Data

Perform Imputation with Constant Value

Perform Imputation with Random Values

Perform Imputation with an mlr Learner

Perform Imputation with Multiple of Minimum

Perform Imputation with Mean Value

Perform Imputation with Median Value

Perform Imputation with Multiple of Minimum

Perform Imputation with Mode Value

Perform Imputation with Normally Distributed Random Values

Perform Imputation with Uniformly Random Values

CPO Learner Object

Log-Transform a Regression Target Variable.

Create Columns from Expressions

Convert Data into Factors Indicating Missing Data

Create a Model Matrix from the Data Given a Formula

Over- or Undersample Binary Classification Tasks

Construct a CPO for PCA Preprocessing

Probability Encoding

Split Numeric Features into Quantile Bins

Train a Model on a Task and Return the Residual Task

Use the se predict.type for response Prediction

Sample Data from a Task

Construct a CPO for Scaling / Centering

Max Abs Scaling CPO

Range Scaling CPO

Drop All Columns Except Certain Selected Ones from Data

Perform SMOTE Oversampling for Binary Classification

Scale Rows to Unit Length

Dummy Function for Documentation Purposes

Get the Retransformation or Inversion Function from a Resulting Object

Transform CPO Hyperparameters

CPO Wrapper

defined to avoid problems with the static type checker

defined to avoid problems with the static type checker

Get the Selection Arguments for Affected CPOs

Get the CPO Class

Get the CPOConstructor Used to Create a CPO Object

Get the ID of a CPO Object

Get the CPO Object's Name

Determine the Operating Type of the CPO

Get the CPO predict.type

Get the Properties of the Given CPO Object

Get the CPOTrained's Capabilities

Get CPO Used to Train a Retrafo / Inverter

Get the Internal State of a CPORetrafo Object

Get the Learner with the CPOs Removed

Get the CPO Associated with a Learner

CPO Composition / Attachment / Application Operator

Check Whether Two CPO are Fundamentally the Same

Internally Used %>>% Operators

Invert Target Preprocessing

Check CPOInverter

Check for NULLCPO

Check CPORetrafo

List all Built-in CPOs

Create a Custom CPO Constructor

Build Data-Dependent CPOs

CPO Multiplexer

Create a CPOTrained with Given Internal State

Composable Preprocessing Operators

CPO Composition Neutral Element

NULLCPO to NULL

NULL to NULLCPO

Turn a list of CPOs into a Single Chained One

Print CPO Objects

Turn the argument list into a ParamSet

Filter randomForestSRC_importance computes the importance of random fo...

Set the ID of a CPO Object

defined to avoid problems with the static type checker