Man pages for tidymodels/recipes
Preprocessing and Feature Engineering Steps for Modeling
| add_step | Add a New Operation to the Current Recipe |
| bake | Apply a trained preprocessing recipe |
| case-weight-helpers | Helpers for steps with case weights |
| case_weights | Using case weights with recipes |
| check_class | Check variable class |
| check_cols | Check if all columns are present |
| check_missing | Check for missing values |
| check_name | check that newly created variable names don't overlap |
| check_new_data | Check for required column at bake-time |
| check_new_values | Check for new values |
| check_range | Check range consistency |
| check_type | Quantitatively check on variables |
| detect_step | Detect if a particular step or check is used in a recipe |
| developer_functions | Developer functions for creating recipes steps |
| discretize | Discretize Numeric Variables |
| fixed | Helper Functions for Profile Data Sets |
| format_ch_vec | Helpers for printing step functions |
| formula.recipe | Create a formula from a prepared recipe |
| fully_trained | Check to see if a recipe is trained/prepared |
| get_data_types | Get types for use in recipes |
| get_keep_original_cols | Get the 'keep_original_cols' value of a recipe step |
| has_role | Role Selection |
| juice | Extract transformed training set |
| names0 | Naming Tools |
| prep | Estimate a preprocessing recipe |
| prepper | Wrapper function for preparing recipes within resampling |
| print.recipe | Print a Recipe |
| rand_id | Make a random identification field for steps |
| recipe | Create a recipe for preprocessing data |
| recipes | recipes: A package for computing and preprocessing design... |
| recipes_eval_select | Evaluate a selection with tidyselect semantics specific to... |
| recipes_extension_check | Checks that steps have all S3 methods |
| recipes-internal | Internal Functions |
| recipes_pkg_check | Update packages |
| recipes_ptype | Prototype of recipe object |
| recipes_ptype_validate | Validate prototype of recipe object |
| recipes_remove_cols | Removes columns if options apply |
| recipes-role-indicator | Role indicators |
| reexports | Objects exported from other packages |
| remove_original_cols | Removes original columns if options apply |
| required_pkgs.recipe | S3 methods for tracking which additional packages are needed... |
| roles | Manually alter roles |
| selections | Methods for selecting variables in step functions |
| sparse_data | Using sparse data with recipes |
| step | 'step' sets the class of the 'step' and 'check' is for... |
| step_arrange | Sort rows using dplyr |
| step_bagimpute | Impute via bagged trees |
| step_bin2factor | Create a factors from A dummy variable |
| step_BoxCox | Box-Cox transformation for non-negative data |
| step_bs | B-spline basis functions |
| step_center | Centering numeric data |
| step_classdist | Distances to class centroids |
| step_classdist_shrunken | Compute shrunken centroid distances for classification models |
| step_corr | High correlation filter |
| step_count | Create counts of patterns using regular expressions |
| step_cut | Cut a numeric variable into a factor |
| step_date | Date feature generator |
| step_depth | Data depths |
| step_discretize | Discretize Numeric Variables |
| step_dummy | Create traditional dummy variables |
| step_dummy_extract | Extract patterns from nominal data |
| step_dummy_multi_choice | Handle levels in multiple predictors together |
| step_factor2string | Convert factors to strings |
| step_filter | Filter rows using dplyr |
| step_filter_missing | Missing value column filter |
| step_geodist | Distance between two locations |
| step_harmonic | Add sin and cos terms for harmonic analysis |
| step_holiday | Holiday feature generator |
| step_hyperbolic | Hyperbolic transformations |
| step_ica | ICA signal extraction |
| step_impute_bag | Impute via bagged trees |
| step_impute_knn | Impute via k-nearest neighbors |
| step_impute_linear | Impute numeric variables via a linear model |
| step_impute_lower | Impute numeric data below the threshold of measurement |
| step_impute_mean | Impute numeric data using the mean |
| step_impute_median | Impute numeric data using the median |
| step_impute_mode | Impute nominal data using the most common value |
| step_impute_roll | Impute numeric data using a rolling window statistic |
| step_indicate_na | Create missing data column indicators |
| step_integer | Convert values to predefined integers |
| step_interact | Create interaction variables |
| step_intercept | Add intercept (or constant) column |
| step_inverse | Inverse transformation |
| step_invlogit | Inverse logit transformation |
| step_isomap | Isomap embedding |
| step_knnimpute | Impute via k-nearest neighbors |
| step_kpca | Kernel PCA signal extraction |
| step_kpca_poly | Polynomial kernel PCA signal extraction |
| step_kpca_rbf | Radial basis function kernel PCA signal extraction |
| step_lag | Create a lagged predictor |
| step_lincomb | Linear combination filter |
| step_log | Logarithmic transformation |
| step_logit | Logit transformation |
| step_lowerimpute | Impute numeric data below the threshold of measurement |
| step_meanimpute | Impute numeric data using the mean |
| step_medianimpute | Impute numeric data using the median |
| step_modeimpute | Impute nominal data using the most common value |
| step_mutate | Add new variables using dplyr |
| step_mutate_at | Mutate multiple columns using dplyr |
| step_naomit | Remove observations with missing values |
| step_nnmf | Non-negative matrix factorization signal extraction |
| step_nnmf_sparse | Non-negative matrix factorization signal extraction with... |
| step_normalize | Center and scale numeric data |
| step_novel | Simple value assignments for novel factor levels |
| step_ns | Natural spline basis functions |
| step_num2factor | Convert numbers to factors |
| step_nzv | Near-zero variance filter |
| step_ordinalscore | Convert ordinal factors to numeric scores |
| step_other | Collapse infrequent categorical levels |
| step_pca | PCA signal extraction |
| step_percentile | Percentile transformation |
| step_pls | Partial least squares feature extraction |
| step_poly | Orthogonal polynomial basis functions |
| step_poly_bernstein | Generalized bernstein polynomial basis |
| step_profile | Create a profiling version of a data set |
| step_range | Scaling numeric data to a specific range |
| step_ratio | Ratio variable creation |
| step_regex | Detect a regular expression |
| step_relevel | Relevel factors to a desired level |
| step_relu | Apply (smoothed) rectified linear transformation |
| step_rename | Rename variables by name using dplyr |
| step_rename_at | Rename multiple columns using dplyr |
| step_rm | General variable filter |
| step_rollimpute | Impute numeric data using a rolling window statistic |
| step_sample | Sample rows using dplyr |
| step_scale | Scaling numeric data |
| step_select | Select variables using dplyr |
| step_shuffle | Shuffle variables |
| step_slice | Filter rows by position using dplyr |
| step_spatialsign | Spatial sign preprocessing |
| step_spline_b | Basis splines |
| step_spline_convex | Convex splines |
| step_spline_monotone | Monotone splines |
| step_spline_natural | Natural splines |
| step_spline_nonnegative | Non-negative splines |
| step_sqrt | Square root transformation |
| step_string2factor | Convert strings to factors |
| step_time | Time feature generator |
| step_unknown | Assign missing categories to "unknown" |
| step_unorder | Convert ordered factors to unordered factors |
| step_window | Moving window functions |
| step_YeoJohnson | Yeo-Johnson transformation |
| step_zv | Zero variance filter |
| summary.recipe | Summarize a recipe |
| terms_select | Select terms in a step function. |
| tidy.recipe | Tidy the result of a recipe |
| update_role_requirements | Update role specific requirements |
| update.step | Update a recipe step |
