Check for Missing Values
Check for New Values
Check if all Columns are Present
Apply a trained preprocessing recipe
Add a New Operation to the Current Recipe
check that newly created variable names don't overlap
Check Variable Class
Detect if a particular step or check is used in a recipe
Check Range Consistency
Quantitatively check on variables
Helpers for printing step functions
Helper Functions for Profile Data Sets
Create a recipe for preprocessing data
Discretize Numeric Variables
Make a random identification field for steps
Role Selection
Extract transformed training set
recipes: A package for computing and preprocessing design matrices.
Check to see if a recipe is trained/prepared
Internal Functions
Create a Formula from a Prepared Recipe
Get the keep_original_cols
value of a recipe step
Evaluate a selection with tidyselect semantics specific to recipes
Update packages
Sort rows using dplyr
Yeo-Johnson Transformation
step
sets the class of the step
and check
is for checks.
Box-Cox Transformation for Non-Negative Data
Centering numeric data
Objects exported from other packages
S3 methods for tracking which additional packages are needed for steps.
Manually Alter Roles
Naming Tools
Estimate a preprocessing recipe
Methods for selecting variables in step functions
Distances to Class Centroids
Handle levels in multiple predictors together
Extract patterns from nominal data
Discretize Numeric Variables
Data Depths
Wrapper function for preparing recipes within resampling
Print a Recipe
B-Spline Basis Functions
Down-Sample a Data Set Based on a Factor Variable
Create a Factors from A Dummy Variable
High Correlation Filter
Create traditional dummy variables
Convert Factors to Strings
Cut a numeric variable into a factor
Hyperbolic Transformations
Filter rows using dplyr
Distance between two locations
Linear Combination Filter
Missing Value Column Filter
Isomap Embedding
Impute numeric data using the mean
Impute numeric data using the median
Impute numeric data using a rolling window statistic
Impute nominal data using the most common value
Impute via k-nearest neighbors
Create a lagged predictor
Remove observations with missing values
Add sin and cos terms for harmonic analysis
Create Counts of Patterns using Regular Expressions
Impute via bagged trees
Date Feature Generator
Create Missing Data Column Indicators
Logarithmic Transformation
Convert values to predefined integers
Logit Transformation
General Variable Filter
Natural Spline Basis Functions
Simple Value Assignments for Novel Factor Levels
Non-Negative Matrix Factorization Signal Extraction
ICA Signal Extraction
Sample rows using dplyr
Summarize a recipe
Polynomial Kernel PCA Signal Extraction
Radial Basis Function Kernel PCA Signal Extraction
Apply (Smoothed) Rectified Linear Transformation
Add intercept (or constant) column
Holiday Feature Generator
Create Interaction Variables
Inverse Transformation
Select Terms in a Step Function.
Tidy the Result of a Recipe
PCA Signal Extraction
Kernel PCA Signal Extraction
Center and scale numeric data
Non-Negative Matrix Factorization Signal Extraction with lasso Penalization
Update a recipe step
Impute numeric data below the threshold of measurement
Select variables using dplyr
Convert Numbers to Factors
Near-Zero Variance Filter
Impute numeric variables via a linear model
Partial Least Squares Feature Extraction
Convert Ordered Factors to Unordered Factors
Percentile Transformation
Orthogonal Polynomial Basis Functions
Scaling Numeric Data
Rename variables by name using dplyr
Ratio Variable Creation
Detect a regular expression
Rename multiple columns using dplyr
Inverse Logit Transformation
Assign missing categories to "unknown"
Spatial Sign Preprocessing
Square Root Transformation
Up-Sample a Data Set Based on a Factor Variable
Convert Ordinal Factors to Numeric Scores
Shuffle Variables
Collapse Some Categorical Levels
Filter rows by position using dplyr
Convert Strings to Factors
Moving Window Functions
Scaling Numeric Data to a Specific Range
Mutate multiple columns using dplyr
Create a Profiling Version of a Data Set
Add new variables using dplyr
Relevel factors to a desired level
Zero Variance Filter