code: def calibration_curve(
      clf=None, X=None, y=None, clf_name="Classifier"
  ):
      """Log a plot depicting how well-calibrated the predicted probabilities of a classifier are.
  
      Also suggests how to calibrate an uncalibrated classifier. Compares estimated predicted
      probabilities by a baseline logistic regression model, the model passed as
      an argument, and by both its isotonic calibration and sigmoid calibrations.
      The closer the calibration curves are to a diagonal the better.
      A sine wave like curve represents an overfitted classifier, while a cosine
      wave like curve represents an underfitted classifier.
      By training isotonic and sigmoid calibrations of the model and comparing
      their curves we can figure out whether the model is over or underfitting and
      if so which calibration (sigmoid or isotonic) might help fix this.
      For more details, see https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html.
  
      Should only be called with a fitted classifer (otherwise an error is thrown).
  
      Please note this function fits variations of the model on the training set when called.
  
      Arguments:
          clf: (clf) Takes in a fitted classifier.
          X: (arr) Training set features.
          y: (arr) Training set labels.
          model_name: (str) Model name. Defaults to 'Classifier'
  
      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_calibration_curve(clf, X, y, "RandomForestClassifier")
      ```
      """
      not_missing = utils.test_missing(clf=clf, X=X, y=y)
      correct_types = utils.test_types(clf=clf, X=X, y=y)
      is_fitted = utils.test_fitted(clf)
      if not_missing and correct_types and is_fitted:
          y = np.asarray(y)
          if y.dtype.char == "U" or not ((y == 0) | (y == 1)).all():
              wandb.termwarn(
                  "This function only supports binary classification at the moment and therefore expects labels to be binary. Skipping calibration curve."
              )
              return
  
          calibration_curve_chart = calculate.calibration_curves(
              clf, X, y, clf_name
          )
  
          wandb.log({"calibration_curve": calibration_curve_chart})
  
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 7 for calling a global function with example usage: calibration_curve(clf, X, y, clf_name) without return types