Machine Learning Models and Tools
Boosting with Classification Trees
Coerce to a Data Frame
Coerce to an MLInput
Coerce to an MLModel
Bayesian Additive Regression Trees Model
Bayesian Additive Regression Trees Model
Gradient Boosting with Regression Trees
C5.0 Decision Trees and Rule-Based Model
Model Calibration
Extract Case Weights
Bagging with Classification Trees
Model Prediction
Print MachineShop Objects
Quadratic Discriminant Analysis Model
Quote Operator
Random Forest Model
Fast Random Forest Model
Set Recipe Roles
Objects exported from other packages
Super Learner Model
Conditional Random Forest Model
Combine MachineShop Objects
Multivariate Adaptive Regression Splines Model
Model Expansion Over Tuning Parameters
SurvMatrix Class Constructors
Model Tuning Grid Expansion
Model Parameters Expansion
Recipe Step Parameters Expansion
Extract Elements of an Object
Flexible and Penalized Discriminant Analysis Models
Model Fitting
GLM Lasso or Elasticnet Model
ModelFrame Class
Tuned Model
Tuning Grid Control
Revert an MLModelFit Object
Variable Importance
Extreme Gradient Boosting Models
Display Model Information
Linear Models
MachineShop: Machine Learning Models and Tools
Mixture Discriminant Analysis Model
Display Performance Metric Information
Performance Metrics
Resampling Controls
MLMetric Class Constructor
MLModel and MLModelFunction Class Constructors
Model Performance Summaries
Parsnip Model
Confusion Matrix
Proportional Hazards Regression Model
Partial Dependence
Model Performance Differences
Discrete Variate Constructors
Gradient Boosting with Additive Models
Generalized Boosted Regression Model
Gradient Boosting with Linear Models
Generalized Linear Model
Model Inputs
Weighted k-Nearest Neighbor Model
Least Angle Regression, Lasso and Infinitesimal Forward Stagewise Mode...
Linear Discriminant Analysis Model
Model Lift Curves
Models
Model Specification
Naive Bayes Classifier Model
Neural Network Model
Tuning Parameters Grid
Model Performance Curves
Model Performance Metrics
Model Performance Plots
Partial Least Squares Model
Ordered Logistic or Probit Regression Model
Resample Estimation of Model Performance
Extract Response Variable
Recursive Feature Elimination
Fast Random Forest (SRC) Model
Recursive Partitioning and Regression Tree Models
Selected Model Inputs
Selected Model
Training Parameters Monitoring Control
Tuning Parameter Optimization
Resampling Prediction Control
Resampling Stratification Control
MachineShop Settings
Stacked Regression Model
K-Means Clustering Variable Reduction
K-Medoids Clustering Variable Selection
Linear Components Variable Reduction
Variable Selection by Filtering
Sparse Principal Components Analysis Variable Reduction
Parametric Survival Model
Support Vector Machine Models
Paired t-Tests for Model Comparisons
Classification and Regression Tree Models
Tuned Model Inputs
Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.
Useful links