# Supported Models and recipes steps

The supported methods currently all come from tidypredict right now.

## Supported models

This table doesn’t exhaustively list fully unsupported models. Please file an issue to add model to table.

Supported Prediction Types
Model | Regression | Classification
--- | --- | ---
parsnip | engine | numeric | class | prob
`boost_tree()` | `"catboost"` | ✅ | ✅ | ✅
`boost_tree()` | `"lightgbm"` | ✅ | ✅ | ✅
`boost_tree()` | `"xgboost"` | ✅ | ✅ | ✅
`cubist_rules()` | `"Cubist"` | ✅ | ❌ | ❌
`decision_tree()` | `"partykit"` | ✅ | ✅ | ✅
`decision_tree()` | `"rpart"` | ✅ | ✅ | ✅
`linear_reg()` | `"lm"` | ✅ | ❌ | ❌
`linear_reg()` | `"glmnet"` | ✅ | ❌ | ❌
`logistic_reg()` | `"glm"` | ❌ | ✅ | ✅
`logistic_reg()` | `"glmnet"` | ❌ | ✅ | ✅
`mars()` | `"earth"` | ✅ | ✅ | ✅
`multinom_reg()` | `"glmnet"` | ❌ | ✅ | ✅
`naive_Bayes()` | `"naivebayes"` | ❌ | ⚪ | ⚪
`nearest_neighbor()` | `any` | ❌ | ❌ | ❌
`rand_forest()` | `"randomForest"` | ✅ | ✅ | ✅
`rand_forest()` | `"ranger"` | ✅ | ✅ | ✅
✅: Supported
❌: Cannot be supported
⚪: Not yet supported

## Recipes steps

The following 52 recipes steps are supported

- `step_BoxCox()`
- `step_YeoJohnson()`
- `step_adasyn()`
- `step_bin2factor()`
- `step_bsmote()`
- `step_center()`
- `step_corr()`
- `step_discretize()`
- `step_downsample()`
- `step_dummy()`
- `step_filter_missing()`
- `step_impute_mean()`
- `step_impute_median()`
- `step_impute_mode()`
- `step_indicate_na()`
- `step_intercept()`
- `step_inverse()`
- `step_lag()`
- `step_lencode_bayes()`
- `step_lencode_glm()`
- `step_lencode_mixed()`
- `step_lincomb()`
- `step_log()`
- `step_mutate()`
- `step_nearmiss()`
- `step_normalize()`
- `step_novel()`
- `step_nzv()`
- `step_other()`
- `step_pca()`
- `step_pca_sparse()`
- `step_pca_sparse_bayes()`
- `step_pca_truncated()`
- `step_range()`
- `step_ratio()`
- `step_rename()`
- `step_rm()`
- `step_rose()`
- `step_scale()`
- `step_select()`
- `step_smote()`
- `step_smotenc()`
- `step_spline_b()`
- `step_spline_convex()`
- `step_spline_monotone()`
- `step_spline_natural()`
- `step_spline_nonnegative()`
- `step_sqrt()`
- `step_tomek()`
- `step_unknown()`
- `step_upsample()`
- `step_zv()`

## tailor adjustments

The following 4 tailor methods are supported

- `tailor::adjust_equivocal_zone()`
- `tailor::adjust_numeric_range()`
- `tailor::adjust_predictions_custom()`
- `tailor::adjust_probability_threshold()`