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")
class_docstr_tok: ["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","''",")"]
docstr: Reads an artifact from the recipe's outputs. Supported artifact names can be obtained by
  examining the recipe graph visualization displayed by
  :py:func:`ClassificationRecipe.inspect()`.
  
  :param artifact_name: The string name of the artifact. Supported artifact values are:
  
                   - ``"ingested_data"``: returns the ingested dataset created in the
                     **ingest** step as a pandas DataFrame.
  
                   - ``"training_data"``: returns the training dataset created in the
                     **split** step as a pandas DataFrame.
  
                   - ``"validation_data"``: returns the validation dataset created in the
                     **split** step as a pandas DataFrame.
  
                   - ``"test_data"``: returns the test dataset created in the **split** step
                     as a pandas DataFrame.
  
                   - ``"ingested_scoring_data"``: returns the scoring dataset created in the
                     **ingest_scoring** step as a pandas DataFrame.
  
                   - ``"transformed_training_data"``: returns the transformed training dataset
                     created in the **transform** step as a pandas DataFrame.
  
                   - ``"transformed_validation_data"``: returns the transformed validation
                     dataset created in the **transform** step as a pandas DataFrame.
  
                   - ``"model"``: returns the MLflow Model recipe created in the **train**
                     step as a :py:class:`PyFuncModel <mlflow.pyfunc.PyFuncModel>` instance.
  
                   - ``"transformer"``: returns the scikit-learn transformer created in the
                     **transform** step.
  
                   - ``"run"``: returns the
                     :py:class:`MLflow Tracking Run <mlflow.entities.Run>` containing the
                     model recipe created in the **train** step and its associated
                     parameters, as well as performance metrics and model explanations created
                     during the **train** and **evaluate** steps.
  
                   - ``"registered_model_version``": returns the MLflow Model Registry
                     :py:class:`ModelVersion <mlflow.entities.model_registry.ModelVersion>`
                     created by the **register** step.
  
                   - ``"scored_data"``: returns the scored dataset created in the
                     **predict** step as a pandas DataFrame.
  
  :return: An object representation of the artifact corresponding to the specified name,
           as described in the ``artifact_name`` parameter docstring. If the artifact is
           not present because its corresponding step has not been executed or its output
           cache has been cleaned, ``None`` is returned.
  
  .. code-block:: python
      :caption: Example
  
      import os
      import pandas as pd
      from mlflow.recipes import Recipe
      from mlflow.pyfunc import PyFuncModel
  
      os.chdir("~/mlp-classification-template")
      classification_recipe = Recipe(profile="local")
      classification_recipe.run()
      train_df: pd.DataFrame = classification_recipe.get_artifact("training_data")
      trained_model: PyFuncModel = classification_recipe.get_artifact("model")
docstr_tok: ["Reads","an","artifact","from","the","recipe","'s","outputs",".","Supported","artifact","names","can","be","obtained","by","examining","the","recipe","graph","visualization","displayed","by",":","py",":","func",":","`","ClassificationRecipe.inspect","(",")","`",".",":","param","artifact_name",":","The","string","name","of","the","artifact",".","Supported","artifact","values","are",":","-","``","``","ingested_data","''","``",":","returns","the","ingested","dataset","created","in","the","*","*","ingest","*","*","step","as","a","pandas","DataFrame",".","-","``","``","training_data","''","``",":","returns","the","training","dataset","created","in","the","*","*","split","*","*","step","as","a","pandas","DataFrame",".","-","``","``","validation_data","''","``",":","returns","the","validation","dataset","created","in","the","*","*","split","*","*","step","as","a","pandas","DataFrame",".","-","``","``","test_data","''","``",":","returns","the","test","dataset","created","in","the","*","*","split","*","*","step","as","a","pandas","DataFrame",".","-","``","``","ingested_scoring_data","''","``",":","returns","the","scoring","dataset","created","in","the","*","*","ingest_scoring","*","*","step","as","a","pandas","DataFrame",".","-","``","``","transformed_training_data","''","``",":","returns","the","transformed","training","dataset","created","in","the","*","*","transform","*","*","step","as","a","pandas","DataFrame",".","-","``","``","transformed_validation_data","''","``",":","returns","the","transformed","validation","dataset","created","in","the","*","*","transform","*","*","step","as","a","pandas","DataFrame",".","-","``","``","model","''","``",":","returns","the","MLflow","Model","recipe","created","in","the","*","*","train","*","*","step","as","a",":","py",":","class",":","`","PyFuncModel","<","mlflow.pyfunc.PyFuncModel",">","`","instance",".","-","``","``","transformer","''","``",":","returns","the","scikit-learn","transformer","created","in","the","*","*","transform","*","*","step",".","-","``","``","run","''","``",":","returns","the",":","py",":","class",":","`","MLflow","Tracking","Run","<","mlflow.entities.Run",">","`","containing","the","model","recipe","created","in","the","*","*","train","*","*","step","and","its","associated","parameters",",","as","well","as","performance","metrics","and","model","explanations","created","during","the","*","*","train","*","*","and","*","*","evaluate","*","*","steps",".","-","``","``","registered_model_version","``","``",":","returns","the","MLflow","Model","Registry",":","py",":","class",":","`","ModelVersion","<","mlflow.entities.model_registry.ModelVersion",">","`","created","by","the","*","*","register","*","*","step",".","-","``","``","scored_data","''","``",":","returns","the","scored","dataset","created","in","the","*","*","predict","*","*","step","as","a","pandas","DataFrame",".",":","return",":","An","object","representation","of","the","artifact","corresponding","to","the","specified","name",",","as","described","in","the","``","artifact_name","``","parameter","docstring",".","If","the","artifact","is","not","present","because","its","corresponding","step","has","not","been","executed","or","its","output","cache","has","been","cleaned",",","``","None","``","is","returned",".","..","code-block",":",":","python",":","caption",":","Example","import","os","import","pandas","as","pd","from","mlflow.recipes","import","Recipe","from","mlflow.pyfunc","import","PyFuncModel","os.chdir","(","``","~\/mlp-classification-template","''",")","classification_recipe","=","Recipe","(","profile=","''","local","''",")","classification_recipe.run","(",")","train_df",":","pd.DataFrame","=","classification_recipe.get_artifact","(","``","training_data","''",")","trained_model",":","PyFuncModel","=","classification_recipe.get_artifact","(","``","model","''",")"]
code: def get_artifact(self, artifact_name: str) -> Optional[Any]:
      """
      Reads an artifact from the recipe's outputs. Supported artifact names can be obtained by
      examining the recipe graph visualization displayed by
      :py:func:`ClassificationRecipe.inspect()`.
  
      :param artifact_name: The string name of the artifact. Supported artifact values are:
  
                       - ``"ingested_data"``: returns the ingested dataset created in the
                         **ingest** step as a pandas DataFrame.
  
                       - ``"training_data"``: returns the training dataset created in the
                         **split** step as a pandas DataFrame.
  
                       - ``"validation_data"``: returns the validation dataset created in the
                         **split** step as a pandas DataFrame.
  
                       - ``"test_data"``: returns the test dataset created in the **split** step
                         as a pandas DataFrame.
  
                       - ``"ingested_scoring_data"``: returns the scoring dataset created in the
                         **ingest_scoring** step as a pandas DataFrame.
  
                       - ``"transformed_training_data"``: returns the transformed training dataset
                         created in the **transform** step as a pandas DataFrame.
  
                       - ``"transformed_validation_data"``: returns the transformed validation
                         dataset created in the **transform** step as a pandas DataFrame.
  
                       - ``"model"``: returns the MLflow Model recipe created in the **train**
                         step as a :py:class:`PyFuncModel <mlflow.pyfunc.PyFuncModel>` instance.
  
                       - ``"transformer"``: returns the scikit-learn transformer created in the
                         **transform** step.
  
                       - ``"run"``: returns the
                         :py:class:`MLflow Tracking Run <mlflow.entities.Run>` containing the
                         model recipe created in the **train** step and its associated
                         parameters, as well as performance metrics and model explanations created
                         during the **train** and **evaluate** steps.
  
                       - ``"registered_model_version``": returns the MLflow Model Registry
                         :py:class:`ModelVersion <mlflow.entities.model_registry.ModelVersion>`
                         created by the **register** step.
  
                       - ``"scored_data"``: returns the scored dataset created in the
                         **predict** step as a pandas DataFrame.
  
      :return: An object representation of the artifact corresponding to the specified name,
               as described in the ``artifact_name`` parameter docstring. If the artifact is
               not present because its corresponding step has not been executed or its output
               cache has been cleaned, ``None`` is returned.
  
      .. code-block:: python
          :caption: Example
  
          import os
          import pandas as pd
          from mlflow.recipes import Recipe
          from mlflow.pyfunc import PyFuncModel
  
          os.chdir("~/mlp-classification-template")
          classification_recipe = Recipe(profile="local")
          classification_recipe.run()
          train_df: pd.DataFrame = classification_recipe.get_artifact("training_data")
          trained_model: PyFuncModel = classification_recipe.get_artifact("model")
      """
      return super().get_artifact(artifact_name=artifact_name)
  
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 4 for calling the ClassificationRecipe obj's underlying member method code with example usage: obj.get_artifact(artifact_name) and returns: super