class_docstr: Linear Rule Regression is a directly interpretable supervised learning
  method that performs linear regression on rule-based features.
class_docstr_tok: ["Linear","Rule","Regression","is","a","directly","interpretable","supervised","learning","method","that","performs","linear","regression","on","rule-based","features","."]
docstr: Predict responses.
  
  Args:
      X (DataFrame): Binarized features with MultiIndex column labels
      Xstd (DataFrame, optional): Standardized numerical features
  Returns:
      array: yhat -- Predicted responses
docstr_tok: ["Predict","responses",".","Args",":","X","(","DataFrame",")",":","Binarized","features","with","MultiIndex","column","labels","Xstd","(","DataFrame",",","optional",")",":","Standardized","numerical","features","Returns",":","array",":","yhat","--","Predicted","responses"]
code: def predict(self, X, Xstd=None):
      """Predict responses.
  
      Args:
          X (DataFrame): Binarized features with MultiIndex column labels
          Xstd (DataFrame, optional): Standardized numerical features
      Returns:
          array: yhat -- Predicted responses
      """
      if len(self.lr.coef_):
          # Compute conjunctions of features
          A = self.compute_conjunctions(X)
          if self.useOrd:
              # Selected ordinal features scaled as in training
              Astd = 0.4 * Xstd.values[:, self.idxNonzeroOrd]
              # Predict probabilities
              B = np.concatenate((Astd, A), axis=1)
              return self.lr.predict(B)
          else:
              # Predict labels
              return self.lr.predict(A)
      else:
          return np.full(X.shape[0], self.mu)
  
file_path: AIX360/aix360/algorithms/rbm/linear_regression.py
imports: import numpy
  import pandas
  from sklearn.linear_model import Lasso, Ridge
  from sklearn.base import BaseEstimator, RegressorMixin
  import matplotlib.pyplot
  from .beam_search import beam_search_K1
label_desc: Use image node_id 4 for calling the LinearRuleRegression obj's underlying member method code with example usage: obj.predict(X, Xstd) and returns: np, self, self