code: def classifier(
      model,
      X_train,
      X_test,
      y_train,
      y_test,
      y_pred,
      y_probas,
      labels,
      is_binary=False,
      model_name="Classifier",
      feature_names=None,
      log_learning_curve=False,
  ):
      """Generate all sklearn classifier plots supported by W&B.
  
      The following plots are generated:
          feature importances, confusion matrix, summary metrics,
          class propotions, calibration curve, roc curve, precision-recall curve.
  
      Should only be called with a fitted classifer (otherwise an error is thrown).
  
      Arguments:
          model: (classifier) Takes in a fitted classifier.
          X_train: (arr) Training set features.
          y_train: (arr) Training set labels.
          X_test: (arr) Test set features.
          y_test: (arr) Test set labels.
          y_pred: (arr) Test set predictions by the model passed.
          y_probas: (arr) Test set predicted probabilities by the model passed.
          labels: (list) Named labels for target varible (y). Makes plots easier to
                          read by replacing target values with corresponding index.
                          For example if `labels=['dog', 'cat', 'owl']` all 0s are
                          replaced by dog, 1s by cat.
          is_binary: (bool) Is the model passed a binary classifier? Defaults to False
          model_name: (str) Model name. Defaults to 'Classifier'
          feature_names: (list) Names for features. Makes plots easier to read by
                                  replacing feature indexes with corresponding names.
          log_learning_curve: (bool) Whether or not to log the learning curve.
                                      Defaults to False.
  
      Returns:
          None: To see plots, go to your W&B run page then expand the 'media' tab
              under 'auto visualizations'.
  
      Example:
      ```python
      wandb.sklearn.plot_classifier(
          model,
          X_train,
          X_test,
          y_train,
          y_test,
          y_pred,
          y_probas,
          ["cat", "dog"],
          False,
          "RandomForest",
          ["barks", "drools", "plays_fetch", "breed"],
      )
      ```
      """
      wandb.termlog("\nPlotting %s." % model_name)
  
      if not isinstance(model, naive_bayes.MultinomialNB):
          feature_importances(model, feature_names)
          wandb.termlog("Logged feature importances.")
  
      if log_learning_curve:
          shared.learning_curve(model, X_train, y_train)
          wandb.termlog("Logged learning curve.")
  
      confusion_matrix(y_test, y_pred, labels)
      wandb.termlog("Logged confusion matrix.")
  
      shared.summary_metrics(
          model, X=X_train, y=y_train, X_test=X_test, y_test=y_test
      )
      wandb.termlog("Logged summary metrics.")
  
      class_proportions(y_train, y_test, labels)
      wandb.termlog("Logged class proportions.")
  
      if not isinstance(model, naive_bayes.MultinomialNB):
          calibration_curve(model, X_train, y_train, model_name)
          wandb.termlog("Logged calibration curve.")
  
      roc(y_test, y_probas, labels)
      wandb.termlog("Logged roc curve.")
  
      precision_recall(y_test, y_probas, labels)
      wandb.termlog("Logged precision-recall curve.")
  
file_path: wandb/wandb/sklearn/plot/classifier.py
imports: from warnings import simplefilter
  import numpy
  from sklearn import naive_bayes
  import wandb
  import wandb.plots
  from wandb.sklearn import calculate, utils
  from .None import shared
label_desc: Use image node_id 1 for calling a global function with example usage: classifier(model, X_train, X_test, y_train, y_test, y_pred, y_probas, labels, is_binary, model_name, feature_names, log_learning_curve) without return types