MachineShop 3.9.2

Version Updates

3.9.2

• Remove dead Rd links to the ParBayesianOptimization package.
• Set required java parameters in the BARTMachineModel example.

MachineShop 3.9.1

Version Updates

3.9.1

• Ensure that global settings are applied to compute nodes when varimp() runs in parallel.
• Compatibility fixes for XGBModel (XGBDARTModel, XGBLinearModel, XGBTreeModel).

MachineShop 3.9.0

Version Updates

3.9.0

• Add offset support to XGBModel.
• Add logical argument pool to calibration() indicating whether to compute a single calibration curve on predictions pooled over all resampling iterations or to compute them for each iteration individually and return the mean calibration curve.
  • The new argument default is pool = FALSE. The pooling that had been the only implementation in previous package versions (<= 3.8.0) can reproduced with pool = FALSE but is deprecated and will be removed along with the argument in a future version.
  • Note that pooling can result in large memory allocation errors when fitting smooth curves with breaks = NULL.

MachineShop 3.8.0

Version Updates

3.8.0

• Changes to varimp() arguments.
  • Add argument sort.
  • Extend argument scale to vectors of logical.
• Changes to model-based variable importance.
  • Fix unused argument error from CForestModel.
  • Use drop1() to compute model term-specific p-values for CoxModel, POLRModel, and SurvRegModel as is done for GLMModel and LMModel.
• Changes to VariableImportance class.
  • Add slots method and metric to store the computational method ("permute" or "model") and the performance metric used for computations.
  • Add update() method to add the new slots to objects created with previous versions of the package.
• Deprecate type = "default" option in predict() and replace it with type = "raw".
• Fix unimplemented type 'list' in 'listgreater' error from SelectedInput.recipe().

MachineShop 3.7.0

Version Updates

3.7.0

• Compatibility updates for parsnip.
• Enable resampling by a grouping variable with BootControl, OOBControl, and SplitControl.
• Enable resampling by a stratification variable with SplitControl.
• Require R 4.1.0 or later.

MachineShop 3.6.2

Version Updates

3.6.2

• Add backward compatibility for older MLModel objects without a na.rm slot.
• Fix CRAN check warning: S3 generic/method consistency.
• Update role_binom(), role_case(), and role_surv() to remove the requirement that their variables be present in newdata supplied to predict().

MachineShop 3.6.1

Version Updates

3.6.1

• Compatibility updates for ggplot2, Matrix, and recipes package dependencies.

MachineShop 3.6.0

Version Updates

3.6.0

• Add argument na.rm to MLModel() for construction of a model that automatically removes all cases with missing values from model fitting and prediction, none, or only those whose missing values are in the response variable. Set the na.rm values in supplied MLModels to automatically remove cases with missing values if not supported by their model fitting and prediction functions.
• Add argument prob.model to SVMModel().
• Add argument verbose to fit() and predict().
• Fix Error in as.data.frame(x) : object 'x' not found issue when fitting a BARTMachineModel that started occurring with bartMachine package version 1.2.7.
• Remove expired deprecations of ModeledInput and rpp().
• Internal changes
  • Add slot na.rm to MLModel.

MachineShop 3.5.0

Version Updates

3.5.0

• Add argument method to r2() for calculation of Pearson or Spearman correlation.
• Add predict() S4 method for MLModelFit.
• Export MLModelFunction().
• Export as.MLInput() methods for MLModelFit and ModelSpecification.
• Export as.MLModel() method for ModelSpecification.
• Improve recursive feature elimination of SelectedInput terms.
• Improve speed of StackedModel and SuperModel.
• Internal changes
  • Add .MachineShop list attribute to MLModelFit.
  • Move field mlmodel in MLModelFit to model in .MachineShop.
  • Move slot input in MLModel to .MachineShop.
  • Pass .MachineShop to the predict and varimp slot functions of MLModel.

MachineShop 3.4.3

Version Updates

3.4.3

• Fix TypeError in dependence() with numeric dummy variables from recipes.
• Prep ModelRecipe with retain = TRUE for recipe steps that are skipped, for example, when test datasets are created.
• Add generalized area under performance curves to auc(), pr_auc(), and roc_auc() for multiclass factor responses.