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