class_docstr: LinearSVC with `predict_proba` method that naively scales
  `decision_function` output for binary classification.
docstr: Min-max scale output of `decision_function` to [0, 1].
docstr_tok: ["Min-max","scale","output","of","`","decision_function","`","to","[","0",",","1","]","."]
code: def predict_proba(self, X):
      """Min-max scale output of `decision_function` to [0, 1]."""
      df = self.decision_function(X)
      calibrated_df = (df - self.df_min_) / (
          self.df_max_ - self.df_min_
      )
      proba_pos_class = np.clip(calibrated_df, 0, 1)
      proba_neg_class = 1 - proba_pos_class
      proba = np.c_[proba_neg_class, proba_pos_class]
      return proba
  
file_path: scikit-learn/examples/calibration/plot_calibration_curve.py
imports: from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  import matplotlib.pyplot
  from matplotlib.gridspec import GridSpec
  from sklearn.calibration import CalibratedClassifierCV, CalibrationDisplay
  from sklearn.linear_model import LogisticRegression
  from sklearn.naive_bayes import GaussianNB
  from collections import defaultdict
  import pandas
  from sklearn.metrics import brier_score_loss, f1_score, log_loss, precision_score, recall_score, roc_auc_score
  import numpy
  from sklearn.svm import LinearSVC
label_desc: Use image node_id 2 for calling the NaivelyCalibratedLinearSVC obj's underlying member method code with example usage: obj.predict_proba(X) and returns: proba