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