class_docstr: [WIP] Random Forest Classifier.
  
      
class_docstr_tok: ["[","WIP","]","Random","Forest","Classifier","."]
docstr: predict_proba.
  
  Predict class probablities for X.
  
  Arguments:
      X: array-like or sparse matrix of shape = [n_samples, n_features]
          The input samples. Internally, its dtype will be converted to
          ``dtype=np.float32``. If a sparse matrix is provided, it will be
          converted into a sparse ``csr_matrix``.
  
  Returns:
      p : array of shape = [n_samples, n_classes], or a list of n_outputs
          such arrays if n_outputs > 1.
          The class probabilities of the input samples. The order of the
          classes corresponds to that in the attribute `classes_`.
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code: def predict_proba(self, X):
      """predict_proba.
  
      Predict class probablities for X.
  
      Arguments:
          X: array-like or sparse matrix of shape = [n_samples, n_features]
              The input samples. Internally, its dtype will be converted to
              ``dtype=np.float32``. If a sparse matrix is provided, it will be
              converted into a sparse ``csr_matrix``.
  
      Returns:
          p : array of shape = [n_samples, n_classes], or a list of n_outputs
              such arrays if n_outputs > 1.
              The class probabilities of the input samples. The order of the
              classes corresponds to that in the attribute `classes_`.
      """
      sc = super(RandomForestClassifier, self)
      return sc.predict(X)
  
imports: from __future__ import division, print_function, absolute_import
  from datetime import datetime
  import os
  import math
  import numpy
  import time
  import tensorflow.compat.v1
  from tensorflow.contrib.tensor_forest.python import tensor_forest
  from tensorflow.contrib.tensor_forest.python.ops import data_ops
  from tensorflow.python.ops import state_ops, array_ops, math_ops
  from ...utils import validate_dim, read_tensor_in_checkpoint
  from ...data_utils import get_num_features, get_num_classes, get_num_sample
  from ...data_flow import generate_data_tensor
  from ..base import BaseEstimator
label_desc: Use image node_id 3 for calling the RandomForestClassifier obj's underlying member method code with example usage: obj.predict_proba(X) and returns: sc