modelConfigs property

Implementation

final List<_ModelConfig> modelConfigs = [
  _ModelConfig(
    key: 'treeEvaluate',
    label: 'Tree',
    checkCommands: [
      ['print(model_rpart)', 'printcp(model_rpart)'],
      ['print(model_ctree)', 'summary(model_ctree)'],
    ],
    checkFiles: [
      ['model_tree_rpart.svg'],
      ['model_tree_ctree.svg'],
    ],
    provider: treeEvaluateProvider,
  ),
  _ModelConfig(
    key: 'forestEvaluate',
    label: 'Forest',
    checkCommands: [
      ['print(model_conditionalForest)', 'print(importance_df)'],
      ['print(model_randomForest)', 'printRandomForests'],
    ],
    checkFiles: [
      ['model_conditional_forest.svg'],
      [
        'model_random_forest_varimp.svg',
        'model_random_forest_error_rate.svg',
        'model_random_forest_oob_roc_curve.svg',
      ],
    ],
    provider: forestEvaluateProvider,
  ),
  _ModelConfig(
    key: 'boostEvaluate',
    label: 'Boost',
    checkCommands: [
      ['print(model_ada)', 'summary(model_ada)'],
      ['print(model_xgb)', 'summary(model_xgb)'],
    ],
    checkFiles: [
      ['model_ada_boost.svg'],
      ['model_xgb_importance.svg'],
    ],
    provider: boostEvaluateProvider,
  ),
  _ModelConfig(
    key: 'svmEvaluate',
    label: 'SVM',
    checkCommands: [
      ['print(svm_model)'],
    ],
    checkFiles: [[]],
    provider: svmEvaluateProvider,
  ),
  _ModelConfig(
    key: 'linearEvaluate',
    label: 'Linear',
    checkCommands: [
      [
        'print(summary(model_glm))',
        'print(anova(model_glm, test = "Chisq"))',
      ],
    ],
    checkFiles: [
      ['model_glm_diagnostic_plots.svg'],
    ],
    provider: linearEvaluateProvider,
  ),
  _ModelConfig(
    key: 'neuralNetEvaluate',
    label: 'Neural',
    checkCommands: [
      ['print(model_neuralnet)', 'summary(model_neuralnet)'],
      ['print(model_nn)', 'summary(model_nn)'],
    ],
    checkFiles: [
      ['model_neuralnet.svg'],
      ['model_nn_nnet.svg'],
    ],
    provider: neuralEvaluateProvider,
  ),
  _ModelConfig(
    key: 'KMeansEvaluate',
    label: 'KMeans',
    checkCommands: [
      ['print(colMeans(tds))'],
    ],
    checkFiles: [
      ['model_cluster_discriminant.svg'],
    ],
    provider: kMeansEvaluateProvider,
  ),
  _ModelConfig(
    key: 'HClustEvaluate',
    label: 'HClust',
    checkCommands: [
      ['print("Within-Cluster Sum of Squares:")'],
    ],
    checkFiles: [
      ['model_cluster_hierarchical.svg'],
    ],
    provider: hClusterEvaluateProvider,
  ),
];