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