Search parsnip models

Find model types, engines, and arguments to fit and predict in the tidymodels framework.

To learn about the parsnip package, see Get Started: Build a Model. Use the filters below to find model types and engines.

"Boosted" ARIMA Regression Models

model: arima_boost engine: arima_xgboost mode: regression package: modeltime

"Boosted" ARIMA Regression Models

model: arima_boost engine: auto_arima_xgboost mode: regression package: modeltime

ADAM Regression Models

model: adam_reg engine: adam mode: regression package: modeltime

ADAM Regression Models

model: adam_reg engine: auto_adam mode: regression package: modeltime

ARIMA Regression Models

model: arima_reg engine: arima mode: regression package: modeltime

ARIMA Regression Models

model: arima_reg engine: auto_arima mode: regression package: modeltime

Automatic machine learning via h2o

model: auto_ml engine: h2o mode: classification package: agua

Automatic machine learning via h2o

model: auto_ml engine: h2o mode: regression package: agua

Bagged MARS via earth

model: bag_mars engine: earth mode: classification package: baguette

Bagged MARS via earth

model: bag_mars engine: earth mode: regression package: baguette

Bagged neural networks via nnet

model: bag_mlp engine: nnet mode: classification package: baguette

Bagged neural networks via nnet

model: bag_mlp engine: nnet mode: regression package: baguette

Bagged trees via C5.0

model: bag_tree engine: C5.0 mode: classification package: baguette

Bagged trees via rpart

model: bag_tree engine: rpart mode: classification package: baguette

Bagged trees via rpart

model: bag_tree engine: rpart mode: regression package: baguette

Bagged trees via rpart

model: bag_tree engine: rpart mode: censored regression package: censored

Bayesian additive regression trees via dbarts

model: bart engine: dbarts mode: classification package: parsnip

Bayesian additive regression trees via dbarts

model: bart engine: dbarts mode: regression package: parsnip

Boosted PROPHET Time Series Models

model: prophet_boost engine: prophet_xgboost mode: regression package: modeltime

Boosted Poisson Trees with Offsets

model: boost_tree_offset engine: xgboost_offset mode: regression package: offsetreg

Boosted trees

model: boost_tree engine: h2o_gbm mode: classification package: agua

Boosted trees

model: boost_tree engine: h2o_gbm mode: regression package: agua

Boosted trees

model: boost_tree engine: mboost mode: censored regression package: censored

Boosted trees via C5.0

model: boost_tree engine: C5.0 mode: classification package: parsnip

Boosted trees via Spark

model: boost_tree engine: spark mode: classification package: parsnip

Boosted trees via Spark

model: boost_tree engine: spark mode: regression package: parsnip

Boosted trees via catboost

model: boost_tree engine: catboost mode: regression package: bonsai

Boosted trees via catboost

model: boost_tree engine: catboost mode: classification package: bonsai

Boosted trees via h2o

model: boost_tree engine: h2o mode: classification package: agua

Boosted trees via h2o

model: boost_tree engine: h2o mode: regression package: agua

Boosted trees via lightgbm

model: boost_tree engine: lightgbm mode: regression package: bonsai

Boosted trees via lightgbm

model: boost_tree engine: lightgbm mode: classification package: bonsai

Boosted trees via xgboost

model: boost_tree engine: xgboost mode: classification package: parsnip

Boosted trees via xgboost

model: boost_tree engine: xgboost mode: regression package: parsnip

Boosted trees via xgboost

model: boost_tree engine: xgboost mode: quantile regression package: parsnip

C5.0 rule-based classification models

model: C5_rules engine: C5.0 mode: classification package: rules

CUBIST Multistep Horizon

model: cubist_multistep engine: cubist_multistep_horizon mode: regression package: rules

Cosinor Regression Models

model: cosinor_reg engine: card mode: regression package: card

Cubist rule-based regression models

model: cubist_rules engine: Cubist mode: regression package: rules

Decision trees via C5.0

model: decision_tree engine: C5.0 mode: classification package: parsnip

Decision trees via CART

model: decision_tree engine: rpart mode: classification package: parsnip

Decision trees via CART

model: decision_tree engine: rpart mode: regression package: parsnip

Decision trees via CART

model: decision_tree engine: rpart mode: censored regression package: censored

Decision trees via Conditional Inference using partykit

model: decision_tree engine: partykit mode: regression package: bonsai

Decision trees via Conditional Inference using partykit

model: decision_tree engine: partykit mode: classification package: bonsai

Decision trees via Conditional Inference using partykit

model: decision_tree engine: partykit mode: censored regression package: censored

Decision trees via Spark

model: decision_tree engine: spark mode: classification package: parsnip

Decision trees via Spark

model: decision_tree engine: spark mode: regression package: parsnip

Discriminant Adaptive Nearest Neighbor Classification

model: nearest_neighbor_adaptive engine: dann mode: classification package: tidydann

Discriminant Adaptive Nearest Neighbor Classification

model: nearest_neighbor_adaptive engine: sub_dann mode: classification package: tidydann

Exponential Smoothing State Space Models

model: exp_smoothing engine: croston mode: regression package: modeltime

Exponential Smoothing State Space Models

model: exp_smoothing engine: ets mode: regression package: modeltime

Exponential Smoothing State Space Models

model: exp_smoothing engine: smooth_es mode: regression package: modeltime

Exponential Smoothing State Space Models

model: exp_smoothing engine: theta mode: regression package: modeltime

Flexible discriminant analysis via earth

model: discrim_flexible engine: earth mode: classification package: discrim

Flexible parametric survival regression

model: survival_reg engine: flexsurvspline mode: censored regression package: censored

GLMNET Multistep Horizon

model: glmnet_multistep engine: glmnet_multistep_horizon mode: regression package: parsnip

Generalized additive models via mgcv

model: gen_additive_mod engine: mgcv mode: regression package: parsnip

Generalized additive models via mgcv

model: gen_additive_mod engine: mgcv mode: classification package: parsnip

Generalized random forests via grf

model: rand_forest engine: grf mode: classification package: parsnip

Generalized random forests via grf

model: rand_forest engine: grf mode: regression package: parsnip

Generalized random forests via grf

model: rand_forest engine: grf mode: quantile regression package: parsnip

K-nearest neighbors via kknn

model: nearest_neighbor engine: kknn mode: classification package: parsnip

K-nearest neighbors via kknn

model: nearest_neighbor engine: kknn mode: regression package: parsnip

Kernel Ridge Regression

model: krr_reg engine: fastkrr mode: regression package: FastKRR

Linear discriminant analysis via James-Stein-type shrinkage estimation

model: discrim_linear engine: sda mode: classification package: discrim

Linear discriminant analysis via MASS

model: discrim_linear engine: MASS mode: classification package: discrim

Linear discriminant analysis via flexible discriminant analysis

model: discrim_linear engine: mda mode: classification package: discrim

Linear discriminant analysis via regularization

model: discrim_linear engine: sparsediscrim mode: classification package: discrim

Linear quantile regression via the quantreg package

model: linear_reg engine: quantreg mode: quantile regression package: parsnip

Linear regression via Bayesian Methods

model: linear_reg engine: stan mode: regression package: parsnip

Linear regression via brulee

model: linear_reg engine: brulee mode: regression package: parsnip

Linear regression via generalized estimating equations (GEE)

model: linear_reg engine: gee mode: regression package: multilevelmod

Linear regression via generalized least squares

model: linear_reg engine: gls mode: regression package: multilevelmod

Linear regression via generalized mixed models

model: linear_reg engine: glmer mode: regression package: multilevelmod

Linear regression via glm

model: linear_reg engine: glm mode: regression package: parsnip

Linear regression via glmnet

model: linear_reg engine: glmnet mode: regression package: parsnip

Linear regression via h2o

model: linear_reg engine: h2o mode: regression package: agua

Linear regression via hierarchical Bayesian methods

model: linear_reg engine: stan_glmer mode: regression package: multilevelmod

Linear regression via keras/tensorflow

model: linear_reg engine: keras mode: regression package: parsnip

Linear regression via keras3

model: linear_reg engine: keras3 mode: regression package: parsnip

Linear regression via lm

model: linear_reg engine: lm mode: regression package: parsnip

Linear regression via mixed models

model: linear_reg engine: lme mode: regression package: multilevelmod

Linear regression via mixed models

model: linear_reg engine: lmer mode: regression package: multilevelmod

Linear regression via spark

model: linear_reg engine: spark mode: regression package: parsnip

Linear support vector machines (SVMs) via LiblineaR

model: svm_linear engine: LiblineaR mode: classification package: parsnip

Linear support vector machines (SVMs) via LiblineaR

model: svm_linear engine: LiblineaR mode: regression package: parsnip

Linear support vector machines (SVMs) via kernlab

model: svm_linear engine: kernlab mode: classification package: parsnip

Linear support vector machines (SVMs) via kernlab

model: svm_linear engine: kernlab mode: regression package: parsnip

Logistic regression via LiblineaR

model: logistic_reg engine: LiblineaR mode: classification package: parsnip

Logistic regression via brulee

model: logistic_reg engine: brulee mode: classification package: parsnip

Logistic regression via generalized estimating equations (GEE)

model: logistic_reg engine: gee mode: classification package: multilevelmod

Logistic regression via glm

model: logistic_reg engine: glm mode: classification package: parsnip

Logistic regression via glmnet

model: logistic_reg engine: glmnet mode: classification package: parsnip

Logistic regression via h2o

model: logistic_reg engine: h2o mode: classification package: agua

Logistic regression via hierarchical Bayesian methods

model: logistic_reg engine: stan_glmer mode: classification package: multilevelmod

Logistic regression via keras

model: logistic_reg engine: keras mode: classification package: parsnip

Logistic regression via keras3

model: logistic_reg engine: keras3 mode: classification package: parsnip

Logistic regression via mixed models

model: logistic_reg engine: glmer mode: classification package: multilevelmod

Logistic regression via spark

model: logistic_reg engine: spark mode: classification package: parsnip

Logistic regression via stan

model: logistic_reg engine: stan mode: classification package: parsnip

MARS Multistep Horizon

model: mars_multistep engine: mars_multistep_horizon mode: regression package: parsnip

MaxEnt model

model: maxent engine: maxnet mode: classification package: tidysdm

Multi-Layer Perceptron (Feedforward Neural Network) via kindling

model: mlp_kindling engine: kindling mode: regression package: kindling

Multi-Layer Perceptron (Feedforward Neural Network) via kindling

model: mlp_kindling engine: kindling mode: classification package: kindling

Multilayer perceptron for quantile regression via qrnn

model: mlp engine: qrnn mode: quantile regression package: parsnip

Multilayer perceptron via brulee

model: mlp engine: brulee mode: classification package: parsnip

Multilayer perceptron via brulee

model: mlp engine: brulee mode: regression package: parsnip

Multilayer perceptron via brulee with two hidden layers

model: mlp engine: brulee_two_layer mode: classification package: parsnip

Multilayer perceptron via brulee with two hidden layers

model: mlp engine: brulee_two_layer mode: regression package: parsnip

Multilayer perceptron via h2o

model: mlp engine: h2o mode: classification package: agua

Multilayer perceptron via h2o

model: mlp engine: h2o mode: regression package: agua

Multilayer perceptron via keras

model: mlp engine: keras mode: classification package: parsnip

Multilayer perceptron via keras

model: mlp engine: keras mode: regression package: parsnip

Multilayer perceptron via keras3

model: mlp engine: keras3 mode: classification package: parsnip

Multilayer perceptron via keras3

model: mlp engine: keras3 mode: regression package: parsnip

Multilayer perceptron via nnet

model: mlp engine: nnet mode: classification package: parsnip

Multilayer perceptron via nnet

model: mlp engine: nnet mode: regression package: parsnip

Multinomial regression via brulee

model: multinom_reg engine: brulee mode: classification package: parsnip

Multinomial regression via glmnet

model: multinom_reg engine: glmnet mode: classification package: parsnip

Multinomial regression via h2o

model: multinom_reg engine: h2o mode: classification package: agua

Multinomial regression via keras

model: multinom_reg engine: keras mode: classification package: parsnip

Multinomial regression via keras3

model: multinom_reg engine: keras3 mode: classification package: parsnip

Multinomial regression via nnet

model: multinom_reg engine: nnet mode: classification package: parsnip

Multinomial regression via spark

model: multinom_reg engine: spark mode: classification package: parsnip

Multiple Seasonality Regression Models (TBATS, STLM)

model: seasonal_reg engine: stlm_arima mode: regression package: modeltime

Multiple Seasonality Regression Models (TBATS, STLM)

model: seasonal_reg engine: stlm_ets mode: regression package: modeltime

Multiple Seasonality Regression Models (TBATS, STLM)

model: seasonal_reg engine: tbats mode: regression package: modeltime

Multivariate adaptive regression splines (MARS) via earth

model: mars engine: earth mode: classification package: parsnip

Multivariate adaptive regression splines (MARS) via earth

model: mars engine: earth mode: regression package: parsnip

NAIVE Forecast Models

model: naive_reg engine: naive mode: regression package: modeltime

NAIVE Forecast Models

model: naive_reg engine: snaive mode: regression package: modeltime

NNETAR Regression Models

model: nnetar_reg engine: nnetar mode: regression package: modeltime

Naive Bayes models via klaR

model: naive_Bayes engine: klaR mode: classification package: discrim

Naive Bayes models via naivebayes

model: naive_Bayes engine: h2o mode: classification package: agua

Naive Bayes models via naivebayes

model: naive_Bayes engine: naivebayes mode: classification package: discrim

Null model

model: null_model engine: parsnip mode: classification package: parsnip

Null model

model: null_model engine: parsnip mode: regression package: parsnip

Oblique random survival forests via aorsf

model: rand_forest engine: aorsf mode: classification package: bonsai

Oblique random survival forests via aorsf

model: rand_forest engine: aorsf mode: regression package: bonsai

Oblique random survival forests via aorsf

model: rand_forest engine: aorsf mode: censored regression package: censored

Ordinal decision trees via CART

model: decision_tree engine: rpartScore mode: classification package: ordered

Ordinal generalized additive model via vector GAMs

model: gen_additive_mod engine: vgam mode: classification package: ordered

Ordinal regression via ordinalNet

model: ordinal_reg engine: ordinalNet mode: classification package: ordered

Ordinal regression via polr

model: ordinal_reg engine: polr mode: classification package: ordered

Ordinal regression via vector GLMs

model: ordinal_reg engine: vglm mode: classification package: ordered

PROPHET Time Series Models

model: prophet_reg engine: prophet mode: regression package: modeltime

Parametric survival regression

model: surv_reg engine: flexsurv mode: regression package: parsnip

Parametric survival regression

model: surv_reg engine: survival mode: regression package: parsnip

Parametric survival regression

model: survival_reg engine: flexsurv mode: censored regression package: censored

Parametric survival regression

model: survival_reg engine: flexsurvcure mode: censored regression package: censored

Parametric survival regression

model: survival_reg engine: survival mode: censored regression package: censored

Parsnip Interface of train_nn()

model: train_nnsnip engine: kindling mode: regression package: kindling

Parsnip Interface of train_nn()

model: train_nnsnip engine: kindling mode: classification package: kindling

Partial least squares via mixOmics

model: pls engine: mixOmics mode: classification package: plsmod

Partial least squares via mixOmics

model: pls engine: mixOmics mode: regression package: plsmod

Poisson Decision Trees with Exposures

model: decision_tree_exposure engine: rpart_exposure mode: regression package: offsetreg

Poisson regression models with offsets

model: poisson_reg_offset engine: glm_offset mode: regression package: poissonreg

Poisson regression models with offsets

model: poisson_reg_offset engine: glmnet_offset mode: regression package: poissonreg

Poisson regression via generalized estimating equations (GEE)

model: poisson_reg engine: gee mode: regression package: multilevelmod

Poisson regression via glm

model: poisson_reg engine: glm mode: regression package: poissonreg

Poisson regression via glmnet

model: poisson_reg engine: glmnet mode: regression package: poissonreg

Poisson regression via h2o

model: poisson_reg engine: h2o mode: regression package: agua

Poisson regression via hierarchical Bayesian methods

model: poisson_reg engine: stan_glmer mode: regression package: multilevelmod

Poisson regression via mixed models

model: poisson_reg engine: glmer mode: regression package: multilevelmod

Poisson regression via pscl

model: poisson_reg engine: hurdle mode: regression package: poissonreg

Poisson regression via pscl

model: poisson_reg engine: zeroinfl mode: regression package: poissonreg

Poisson regression via stan

model: poisson_reg engine: stan mode: regression package: poissonreg

Polynomial support vector machines (SVMs) via kernlab

model: svm_poly engine: kernlab mode: classification package: parsnip

Polynomial support vector machines (SVMs) via kernlab

model: svm_poly engine: kernlab mode: regression package: parsnip

Proportional hazards regression

model: proportional_hazards engine: glmnet mode: censored regression package: censored

Proportional hazards regression

model: proportional_hazards engine: survival mode: censored regression package: censored

Quadratic discriminant analysis via MASS

model: discrim_quad engine: MASS mode: classification package: discrim

Quadratic discriminant analysis via regularization

model: discrim_quad engine: sparsediscrim mode: classification package: discrim

Radial basis function support vector machines

model: svm_rbf engine: liquidSVM mode: classification package: parsnip

Radial basis function support vector machines

model: svm_rbf engine: liquidSVM mode: regression package: parsnip

Radial basis function support vector machines (SVMs) via kernlab

model: svm_rbf engine: kernlab mode: classification package: parsnip

Radial basis function support vector machines (SVMs) via kernlab

model: svm_rbf engine: kernlab mode: regression package: parsnip

Random forests via Conditional Inference using partykit

model: rand_forest engine: partykit mode: regression package: bonsai

Random forests via Conditional Inference using partykit

model: rand_forest engine: partykit mode: classification package: bonsai

Random forests via Conditional Inference using partykit

model: rand_forest engine: partykit mode: censored regression package: censored

Random forests via h2o

model: rand_forest engine: h2o mode: classification package: agua

Random forests via h2o

model: rand_forest engine: h2o mode: regression package: agua

Random forests via ordinalForest

model: rand_forest engine: ordinalForest mode: classification package: ordered

Random forests via randomForest

model: rand_forest engine: randomForest mode: classification package: parsnip

Random forests via randomForest

model: rand_forest engine: randomForest mode: regression package: parsnip

Random forests via ranger

model: rand_forest engine: ranger mode: classification package: parsnip

Random forests via ranger

model: rand_forest engine: ranger mode: regression package: parsnip

Random forests via spark

model: rand_forest engine: spark mode: classification package: parsnip

Random forests via spark

model: rand_forest engine: spark mode: regression package: parsnip

Recurrent Neural Network via kindling

model: rnn_kindling engine: kindling mode: regression package: kindling

Recurrent Neural Network via kindling

model: rnn_kindling engine: kindling mode: classification package: kindling

Regularized discriminant analysis via klaR

model: discrim_regularized engine: klaR mode: classification package: discrim

RuleFit models via h2o

model: rule_fit engine: h2o mode: classification package: agua

RuleFit models via h2o

model: rule_fit engine: h2o mode: regression package: agua

RuleFit models via xrf

model: rule_fit engine: xrf mode: classification package: rules

RuleFit models via xrf

model: rule_fit engine: xrf mode: regression package: rules

SVM-POLY Multistep Horizon

model: svm_poly_multistep engine: svm_poly_multistep_horizon mode: regression package: parsnip

SVM-RBF Multistep Horizon

model: svm_rbf_multistep engine: svm_rbf_multistep_horizon mode: regression package: parsnip

Temporal Hierarchical Forecasting (THIEF) Models

model: temporal_hierarchy engine: thief mode: regression package: modeltime

Window Forecast Models

model: window_reg engine: window_function mode: regression package: modeltime

XGBOOST Multistep Horizon

model: xgboost_multistep engine: xgboost_multistep_horizon mode: regression package: modeltime