Quantitatively check on variables
Detect if a particular step or check is used in a recipe
Add a New Operation to the Current Recipe
Apply a Trained Data Recipe
Check Variable Class
Check for Missing Values
Check for New Values
Check if all Columns are Present
Check Range Consistency
check that newly created variable names don't overlap
Train a Data Recipe
Wrapper function for preparing recipes within resampling
Discretize Numeric Variables
Yeo-Johnson Transformation
Helper Functions for Profile Data Sets
Sort rows using dplyr
Create a Formula from a Prepared Recipe
Check to see if a recipe is trained/prepared
Create a Recipe for Preprocessing Data
Internal Functions
Helpers for printing step functions
Role Selection
recipes: A package for computing and preprocessing design matrices.
Manually Alter Roles
Extract Finalized Training Set
Naming Tools
Filter rows using dplyr
Logarithmic Transformation
ICA Signal Extraction
Distance between two locations
Logit Transformation
Imputation of numeric variables via a linear model.
Print a Recipe
Add intercept (or constant) column
Cut a numeric variable into a factor
Create Counts of Patterns using Regular Expressions
Inverse Transformation
Update packages
step
sets the class of the step
and check
is for checks.
Impute Nominal Data Using the Most Common Value
Impute Numeric Data Using the Median
Partial Least Squares Feature Extraction
Orthogonal Polynomial Basis Functions
Box-Cox Transformation for Non-Negative Data
Imputation via K-Nearest Neighbors
Data Depths
Date Feature Generator
Make a random identification field for steps
High Correlation Filter
Distances to Class Centroids
Radial Basis Function Kernel PCA Signal Extraction
Polynomial Kernel PCA Signal Extraction
Create a Profiling Version of a Data Set
Kernel PCA Signal Extraction
Simple Value Assignments for Novel Factor Levels
Methods for Selecting Variables in Step Functions
Scaling Numeric Data to a Specific Range
Center and scale numeric data
Near-Zero Variance Filter
Filter rows by position using dplyr
Shuffle Variables
Imputation via Bagged Trees
Dummy Variables Creation
S3 methods for tracking which additional packages are needed for steps.
Convert Factors to Strings
Create a Factors from A Dummy Variable
Holiday Feature Generator
B-Spline Basis Functions
Objects exported from other packages
Centering numeric data
Convert Ordinal Factors to Numeric Scores
Rename variables by name
Discretize Numeric Variables
Tidy the Result of a Recipe
Assign missing categories to "unknown"
Rename multiple columns
Convert Strings to Factors
Find recommended methods for generating parameter values
Hyperbolic Transformations
Summarize a Recipe
Convert values to predefined integers
Down-Sample a Data Set Based on a Factor Variable
Isomap Embedding
Inverse Logit Transformation
Mutate multiple columns
Select Terms in a Step Function.
Ratio Variable Creation
Sample rows using dplyr
Add new variables using mutate
Create Dummy Variables using Regular Expressions
Impute Numeric Data Below the Threshold of Measurement
Zero Variance Filter
Scaling Numeric Data
Moving Window Functions
Create a lagged predictor
Collapse Some Categorical Levels
Remove observations with missing values
NNMF Signal Extraction
PCA Signal Extraction
Impute Numeric Data Using the Mean
Create Interaction Variables
Natural Spline Basis Functions
Linear Combination Filter
Relevel factors to a desired level
Convert Numbers to Factors
Spatial Sign Preprocessing
Apply (Smoothed) Rectified Linear Transformation
Update a recipe step
General Variable Filter
Square Root Transformation
Convert Ordered Factors to Unordered Factors
Impute Numeric Data Using a Rolling Window Statistic
Up-Sample a Data Set Based on a Factor Variable