
Function reference
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recipes
recipes-package
- recipes: A package for computing and preprocessing design matrices.
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recipe()
- Create a recipe for preprocessing data
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formula(<recipe>)
- Create a Formula from a Prepared Recipe
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print(<recipe>)
- Print a Recipe
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summary(<recipe>)
- Summarize a recipe
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prep()
- Estimate a preprocessing recipe
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bake()
- Apply a trained preprocessing recipe
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juice()
- Extract transformed training set
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selections
selection
- Methods for selecting variables in step functions
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has_role()
has_type()
all_outcomes()
all_predictors()
all_date()
all_date_predictors()
all_datetime()
all_datetime_predictors()
all_double()
all_double_predictors()
all_factor()
all_factor_predictors()
all_integer()
all_integer_predictors()
all_logical()
all_logical_predictors()
all_nominal()
all_nominal_predictors()
all_numeric()
all_numeric_predictors()
all_ordered()
all_ordered_predictors()
all_string()
all_string_predictors()
all_unordered()
all_unordered_predictors()
current_info()
- Role Selection
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add_role()
update_role()
remove_role()
- Manually Alter Roles
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update_role_requirements()
- Update role specific requirements
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get_case_weights()
averages()
medians()
variances()
correlations()
covariances()
pca_wts()
are_weights_used()
- Helpers for steps with case weights
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case_weights
- Using case weights with recipes
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step_impute_bag()
step_bagimpute()
imp_vars()
- Impute via bagged trees
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step_impute_knn()
step_knnimpute()
- Impute via k-nearest neighbors
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step_impute_linear()
- Impute numeric variables via a linear model
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step_impute_lower()
step_lowerimpute()
- Impute numeric data below the threshold of measurement
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step_impute_mean()
step_meanimpute()
- Impute numeric data using the mean
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step_impute_median()
step_medianimpute()
- Impute numeric data using the median
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step_impute_mode()
step_modeimpute()
- Impute nominal data using the most common value
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step_impute_roll()
step_rollimpute()
- Impute numeric data using a rolling window statistic
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step_unknown()
- Assign missing categories to "unknown"
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step_BoxCox()
- Box-Cox Transformation for Non-Negative Data
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step_bs()
- B-Spline Basis Functions
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step_harmonic()
- Add sin and cos terms for harmonic analysis
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step_hyperbolic()
- Hyperbolic Transformations
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step_inverse()
- Inverse Transformation
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step_invlogit()
- Inverse Logit Transformation
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step_log()
- Logarithmic Transformation
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step_logit()
- Logit Transformation
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step_mutate()
- Add new variables using dplyr
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step_ns()
- Natural Spline Basis Functions
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step_poly()
- Orthogonal Polynomial Basis Functions
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step_poly_bernstein()
- Generalized Bernstein Polynomial Basis
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step_relu()
- Apply (Smoothed) Rectified Linear Transformation
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step_spline_b()
- Basis Splines
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step_spline_convex()
- Convex Splines
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step_spline_monotone()
- Monotone Splines
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step_spline_natural()
- Natural Splines
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step_spline_nonnegative()
- Non-Negative Splines
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step_sqrt()
- Square Root Transformation
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step_YeoJohnson()
- Yeo-Johnson Transformation
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step_discretize()
- Discretize Numeric Variables
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discretize()
predict(<discretize>)
- Discretize Numeric Variables
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step_cut()
- Cut a numeric variable into a factor
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step_bin2factor()
- Create a Factors from A Dummy Variable
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step_count()
- Create Counts of Patterns using Regular Expressions
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step_date()
- Date Feature Generator
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step_dummy()
- Create traditional dummy variables
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step_dummy_extract()
- Extract patterns from nominal data
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step_dummy_multi_choice()
- Handle levels in multiple predictors together
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step_factor2string()
- Convert Factors to Strings
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step_holiday()
- Holiday Feature Generator
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step_indicate_na()
- Create Missing Data Column Indicators
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step_integer()
- Convert values to predefined integers
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step_novel()
- Simple Value Assignments for Novel Factor Levels
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step_num2factor()
- Convert Numbers to Factors
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step_ordinalscore()
- Convert Ordinal Factors to Numeric Scores
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step_other()
- Collapse Some Categorical Levels
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step_percentile()
- Percentile Transformation
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step_regex()
- Detect a regular expression
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step_relevel()
- Relevel factors to a desired level
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step_string2factor()
- Convert Strings to Factors
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step_time()
- Time Feature Generator
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step_unknown()
- Assign missing categories to "unknown"
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step_unorder()
- Convert Ordered Factors to Unordered Factors
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step_interact()
- Create Interaction Variables
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step_center()
- Centering numeric data
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step_normalize()
- Center and scale numeric data
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step_range()
- Scaling Numeric Data to a Specific Range
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step_scale()
- Scaling Numeric Data
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step_classdist()
- Distances to Class Centroids
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step_depth()
- Data Depths
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step_geodist()
- Distance between two locations
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step_ica()
- ICA Signal Extraction
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step_isomap()
- Isomap Embedding
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step_kpca()
- Kernel PCA Signal Extraction
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step_kpca_poly()
- Polynomial Kernel PCA Signal Extraction
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step_kpca_rbf()
- Radial Basis Function Kernel PCA Signal Extraction
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step_mutate_at()
- Mutate multiple columns using dplyr
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step_nnmf()
- Non-Negative Matrix Factorization Signal Extraction
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step_nnmf_sparse()
- Non-Negative Matrix Factorization Signal Extraction with lasso Penalization
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step_pca()
- PCA Signal Extraction
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step_pls()
- Partial Least Squares Feature Extraction
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step_ratio()
denom_vars()
- Ratio Variable Creation
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step_spatialsign()
- Spatial Sign Preprocessing
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step_corr()
- High Correlation Filter
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step_filter_missing()
- Missing Value Column Filter
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step_lincomb()
- Linear Combination Filter
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step_nzv()
- Near-Zero Variance Filter
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step_rm()
- General Variable Filter
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step_select()
- Select variables using dplyr
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step_zv()
- Zero Variance Filter
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step_arrange()
- Sort rows using dplyr
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step_filter()
- Filter rows using dplyr
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step_lag()
- Create a lagged predictor
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step_naomit()
- Remove observations with missing values
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step_impute_roll()
step_rollimpute()
- Impute numeric data using a rolling window statistic
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step_sample()
- Sample rows using dplyr
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step_shuffle()
- Shuffle Variables
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step_slice()
- Filter rows by position using dplyr
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step_intercept()
- Add intercept (or constant) column
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step_profile()
- Create a Profiling Version of a Data Set
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step_rename()
- Rename variables by name using dplyr
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step_rename_at()
- Rename multiple columns using dplyr
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step_window()
- Moving Window Functions
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check_class()
- Check Variable Class
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check_cols()
- Check if all Columns are Present
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check_missing()
- Check for Missing Values
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check_new_values()
- Check for New Values
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check_range()
- Check Range Consistency
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add_step()
add_check()
- Add a New Operation to the Current Recipe
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detect_step()
- Detect if a particular step or check is used in a recipe
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fully_trained()
- Check to see if a recipe is trained/prepared
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.get_data_types()
- Get types for use in recipes
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names0()
dummy_names()
dummy_extract_names()
- Naming Tools
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prepper()
- Wrapper function for preparing recipes within resampling
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recipes_eval_select()
- Evaluate a selection with tidyselect semantics specific to recipes
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recipes_extension_check()
- Checks that steps have all S3 methods
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update(<step>)
- Update a recipe step
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tidy(<step_BoxCox>)
tidy(<step_YeoJohnson>)
tidy(<step_arrange>)
tidy(<step_bin2factor>)
tidy(<step_bs>)
tidy(<step_center>)
tidy(<check_class>)
tidy(<step_classdist>)
tidy(<check_cols>)
tidy(<step_corr>)
tidy(<step_count>)
tidy(<step_cut>)
tidy(<step_date>)
tidy(<step_depth>)
tidy(<step_discretize>)
tidy(<step_dummy>)
tidy(<step_dummy_multi_choice>)
tidy(<step_dummy_extract>)
tidy(<step_factor2string>)
tidy(<step_filter>)
tidy(<step_filter_missing>)
tidy(<step_geodist>)
tidy(<step_harmonic>)
tidy(<step_holiday>)
tidy(<step_hyperbolic>)
tidy(<step_ica>)
tidy(<step_impute_bag>)
tidy(<step_impute_knn>)
tidy(<step_impute_linear>)
tidy(<step_impute_lower>)
tidy(<step_impute_mean>)
tidy(<step_impute_median>)
tidy(<step_impute_mode>)
tidy(<step_impute_roll>)
tidy(<step_integer>)
tidy(<step_interact>)
tidy(<step_intercept>)
tidy(<step_inverse>)
tidy(<step_invlogit>)
tidy(<step_isomap>)
tidy(<step_kpca>)
tidy(<step_kpca_poly>)
tidy(<step_kpca_rbf>)
tidy(<step_lag>)
tidy(<step_lincomb>)
tidy(<step_log>)
tidy(<step_logit>)
tidy(<check_missing>)
tidy(<step_mutate>)
tidy(<step_mutate_at>)
tidy(<step_indicate_na>)
tidy(<step_naomit>)
tidy(<check_new_values>)
tidy(<step_nnmf>)
tidy(<step_nnmf_sparse>)
tidy(<step_normalize>)
tidy(<step_novel>)
tidy(<step_ns>)
tidy(<step_num2factor>)
tidy(<step_nzv>)
tidy(<step_ordinalscore>)
tidy(<step_other>)
tidy(<step_pca>)
tidy(<step_percentile>)
tidy(<step_pls>)
tidy(<step_poly>)
tidy(<step_poly_bernstein>)
tidy(<step_profile>)
tidy(<step_range>)
tidy(<check_range>)
tidy(<step_ratio>)
tidy(<step_regex>)
tidy(<step_relevel>)
tidy(<step_relu>)
tidy(<step_rename>)
tidy(<step_rename_at>)
tidy(<step_rm>)
tidy(<step_sample>)
tidy(<step_scale>)
tidy(<step_select>)
tidy(<step_shuffle>)
tidy(<step_slice>)
tidy(<step_spatialsign>)
tidy(<step_spline_b>)
tidy(<step_spline_convex>)
tidy(<step_spline_monotone>)
tidy(<step_spline_natural>)
tidy(<step_spline_nonnegative>)
tidy(<step_sqrt>)
tidy(<step_string2factor>)
tidy(<recipe>)
tidy(<step>)
tidy(<check>)
tidy(<step_time>)
tidy(<step_unknown>)
tidy(<step_unorder>)
tidy(<step_window>)
tidy(<step_zv>)
- Tidy the Result of a Recipe