class_docstr: Super class of RegressionTree and ClassificationTree.
  
  Parameters:
  -----------
  min_samples_split: int
      The minimum number of samples needed to make a split when building a tree.
  min_impurity: float
      The minimum impurity required to split the tree further.
  max_depth: int
      The maximum depth of a tree.
  loss: function
      Loss function that is used for Gradient Boosting models to calculate impurity.
class_docstr_tok: ["Super","class","of","RegressionTree","and","ClassificationTree",".","Parameters",":","--","--","--","--","--","-","min_samples_split",":","int","The","minimum","number","of","samples","needed","to","make","a","split","when","building","a","tree",".","min_impurity",":","float","The","minimum","impurity","required","to","split","the","tree","further",".","max_depth",":","int","The","maximum","depth","of","a","tree",".","loss",":","function","Loss","function","that","is","used","for","Gradient","Boosting","models","to","calculate","impurity","."]
docstr: Classify samples one by one and return the set of labels 
docstr_tok: ["Classify","samples","one","by","one","and","return","the","set","of","labels"]
code: def predict(self, X):
      """Classify samples one by one and return the set of labels"""
      y_pred = [self.predict_value(sample) for sample in X]
      return y_pred
  
imports: from __future__ import division, print_function
  import numpy
  from mlfromscratch.utils import divide_on_feature, train_test_split, standardize, mean_squared_error
  from mlfromscratch.utils import calculate_entropy, accuracy_score, calculate_variance
label_desc: Use image node_id 5 for calling the DecisionTree obj's underlying member method code with example usage: obj.predict(X) and returns: y_pred