Analysis Functions

Training

fit Model fitting
resample Resample estimation of model performance

 

Tuning Grids

expand_model Model expansion over tuning parameters
expand_modelgrid Model tuning grid expansion
expand_params Model parameters expansion
expand_steps Recipe step parameters expansion

 

Response Values

response Observed
predict Predicted

 

Performance Assessment

calibration Model calibration
confusion Confusion matrices
dependence Partial dependence
diff Model performance differences
lift Lift curves
performance Model performance metrics
performance_curve Model performance curves
rfe Recursive feature elimination
varimp Variable importance

 

Resample Control Structures

BootControl Simple bootstrap
BootOptimismControl Optimism-corrected bootstrap
CVControl Repeated K-fold cross-validation
CVOptimismControl Optimism-corrected cross-validation
OOBControl Out-of-bootstrap
SplitControl Split training-testing
TrainControl Training resubstitution

 

Analysis Summaries

plot Graphical summaries
print Object summaries
summary Tabular summaries

 

Information

metricinfo Metric function names and attributes
modelinfo Model constructor names and attributes
settings Global settings

 

Package Extension

MLMetric Metric class constructor
MLModel Model class constructor

 

Model Constructor Functions

Package-supplied model constructor functions and supported response variable types.
Response Variable Types
Function Label Categorical1 Continuous2 Survival3
AdaBagModel Bagging with Classification Trees f
AdaBoostModel Boosting with Classification Trees f
BARTMachineModel Bayesian Additive Regression Trees b n
BARTModel Bayesian Additive Regression Trees f n S
BlackBoostModel Gradient Boosting with Regression Trees b n S
C50Model C5.0 Classification f
CForestModel Conditional Random Forests f n S
CoxModel Cox Regression S
CoxStepAICModel Cox Regression (Stepwise) S
EarthModel Multivariate Adaptive Regression Splines f n
FDAModel Flexible Discriminant Analysis f
GAMBoostModel Gradient Boosting with Additive Models b n S
GBMModel Generalized Boosted Regression f n S
GLMBoostModel Gradient Boosting with Linear Models b n S
GLMModel Generalized Linear Models f m, n
GLMStepAICModel Generalized Linear Models (Stepwise) b n
GLMNetModel Lasso and Elastic-Net f m, n S
KNNModel K-Nearest Neighbors Model f, o n
LARSModel Least Angle Regression n
LDAModel Linear Discriminant Analysis f
LMModel Linear Model f m, n
MDAModel Mixture Discriminant Analysis f
NaiveBayesModel Naive Bayes Classifier f
NNetModel Feed-Forward Neural Networks f n
ParsnipModel null_model(mode = “unknown”, engine = “parsnip”) f m, n S
PDAModel Penalized Discriminant Analysis f
PLSModel Partial Least Squares f n
POLRModel Ordered Logistic Regression o
QDAModel Quadratic Discriminant Analysis f
RandomForestModel Random Forests f n
RangerModel Fast Random Forests f n S
RFSRCModel Random Forest (SRC) f m, n S
RPartModel Recursive Partitioning and Regression Trees f n S
SelectedModel Selected Model b, f, o m, n S
StackedModel Stacked Regression b, f, o m, n S
SuperModel Super Learner f n S
SurvRegModel Parametric Survival S
SurvRegStepAICModel Parametric Survival (Stepwise) S
SVMModel Support Vector Machines f n
SVMANOVAModel Support Vector Machines (ANOVA) f n
SVMBesselModel Support Vector Machines (Bessel) f n
SVMLaplaceModel Support Vector Machines (Laplace) f n
SVMLinearModel Support Vector Machines (Linear) f n
SVMPolyModel Support Vector Machines (Poly) f n
SVMRadialModel Support Vector Machines (Radial) f n
SVMSplineModel Support Vector Machines (Spline) f n
SVMTanhModel Support Vector Machines (Tanh) f n
TreeModel Regression and Classification Trees f n
TunedModel Grid Tuned NullModel b, f, o m, n S
XGBModel Extreme Gradient Boosting f n S
XGBDARTModel Extreme Gradient Boosting (DART) f n S
XGBLinearModel Extreme Gradient Boosting (Linear) f n S
XGBTreeModel Extreme Gradient Boosting (Tree) f n S
1 b = binary factor, f = factor, o = ordered factor
2 m = matrix, n = numeric
3 S = Surv

Metric Functions

Package-supplied performance metric functions and supported response variable types.
Response Variable Types
Function Label Categorical1 Continuous2 Survival3
accuracy Accuracy f S
auc Area Under Performance Curve f S
brier Brier Score f S
cindex Concordance Index b S
cross_entropy Cross Entropy f
f_score F Score b S
fnr False Negative Rate b S
fpr False Positive Rate b S
gini Gini Coefficient m, n S
kappa2 Cohen’s Kappa f S
mae Mean Absolute Error m, n S
mse Mean Squared Error m, n S
msle Mean Squared Log Error m, n S
npv Negative Predictive Value b S
ppr Positive Prediction Rate b S
ppv Positive Predictive Value b S
pr_auc Area Under Precision-Recall Curve f S
precision Precision b S
r2 Coefficient of Determination m, n S
recall Recall b S
rmse Root Mean Squared Error m, n S
rmsle Root Mean Squared Log Error m, n S
roc_auc Area Under ROC Curve f S
roc_index ROC Index b S
sensitivity Sensitivity b S
specificity Specificity b S
tnr True Negative Rate b S
tpr True Positive Rate b S
weighted_kappa2 Weighted Cohen’s Kappa o
1 b = binary factor, f = factor, o = ordered factor
2 m = matrix, n = numeric
3 S = Surv