class_docstr: A recipe for developing high-quality classification models. The recipe is designed for
  developing models using scikit-learn and frameworks that integrate with scikit-learn,
  such as the ``XGBClassifier`` API from XGBoost.
  The training recipe contains the following sequential steps:
  
  **ingest** -> **split** -> **transform** -> **train** -> **evaluate** -> **register**
  
  while the batch scoring recipe contains this set of sequential steps:
  
  **ingest_scoring** -> **predict**
  
  .. code-block:: python
      :caption: Example
  
      import os
      from mlflow.recipes import Recipe
  
      os.chdir("~/mlp-classification-template")
      classification_recipe = Recipe(profile="local")
      # Display a visual overview of the recipe graph
      classification_recipe.inspect()
      # Run the full recipe
      classification_recipe.run()
      # Display a summary of results from the 'train' step, including the trained model
      # and associated performance metrics computed from the training & validation datasets
      classification_recipe.inspect(step="train")
      # Display a summary of results from the 'evaluate' step, including model explanations
      # computed from the validation dataset and metrics computed from the test dataset
      classification_recipe.inspect(step="evaluate")
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docstr: Displays a visual overview of the recipe graph, or displays a summary of results from
  a particular recipe step if specified. If the specified step has not been executed,
  nothing is displayed.
  
  :param step: String name of the recipe step for which to display a results summary. If
               unspecified, a visual overview of the recipe graph is displayed.
  
  .. code-block:: python
      :caption: Example
  
      import os
      from mlflow.recipes import Recipe
  
      os.chdir("~/mlp-classification-template")
      classification_recipe = Recipe(profile="local")
      # Display a visual overview of the recipe graph.
      classification_recipe.inspect()
      # Run the 'train' recipe step
      classification_recipe.run(step="train")
      # Display a summary of results from the preceding 'transform' step
      classification_recipe.inspect(step="transform")
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code: def inspect(self, step: Optional[str] = None) -> None:
      """
      Displays a visual overview of the recipe graph, or displays a summary of results from
      a particular recipe step if specified. If the specified step has not been executed,
      nothing is displayed.
  
      :param step: String name of the recipe step for which to display a results summary. If
                   unspecified, a visual overview of the recipe graph is displayed.
  
      .. code-block:: python
          :caption: Example
  
          import os
          from mlflow.recipes import Recipe
  
          os.chdir("~/mlp-classification-template")
          classification_recipe = Recipe(profile="local")
          # Display a visual overview of the recipe graph.
          classification_recipe.inspect()
          # Run the 'train' recipe step
          classification_recipe.run(step="train")
          # Display a summary of results from the preceding 'transform' step
          classification_recipe.inspect(step="transform")
      """
      super().inspect(step=step)
  
file_path: mlflow/mlflow/recipes/classification/v1/recipe.py
imports: import logging
  from typing import Any, Optional
  from mlflow.recipes.recipe import BaseRecipe
  from mlflow.recipes.step import BaseStep
  from mlflow.recipes.steps.evaluate import EvaluateStep
  from mlflow.recipes.steps.ingest import IngestScoringStep, IngestStep
  from mlflow.recipes.steps.predict import PredictStep
  from mlflow.recipes.steps.register import RegisterStep
  from mlflow.recipes.steps.split import SplitStep
  from mlflow.recipes.steps.train import TrainStep
  from mlflow.recipes.steps.transform import TransformStep
label_desc: Use image node_id 6 for calling the ClassificationRecipe obj's underlying member method code with example usage: obj.inspect(step) without return types