class_docstr: LinearSVC with `predict_proba` method that naively scales
  `decision_function` output.
class_docstr_tok: ["LinearSVC","with","`","predict_proba","`","method","that","naively","scales","`","decision_function","`","output","."]
code: def fit(self, X, y):
      super().fit(X, y)
      df = self.decision_function(X)
      self.df_min_ = df.min()
      self.df_max_ = df.max()
  
file_path: scikit-learn/examples/calibration/plot_compare_calibration.py
imports: from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  import numpy
  from sklearn.svm import LinearSVC
  from sklearn.calibration import CalibrationDisplay
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.naive_bayes import GaussianNB
  import matplotlib.pyplot
  from matplotlib.gridspec import GridSpec
label_desc: Use image node_id 1 for calling the NaivelyCalibratedLinearSVC obj's underlying member method code with example usage: obj.fit(X, y) without return types