title: Splitting dataset to 90,10 instead of n_folds
body: <p>I use the randomforest algorithm. I would like to use the following code. Instead of evaluating the algorithm through n_folds, I would like to split it into 90% for train and 10% for test.
  I changed the <code>n_folds</code> into <code>n_folds=1</code> and added the following lines:</p>
  
  <pre><code>train, test = train_test_split(dataset1, test_size=0.1, random_state = 0) ###&lt;-----
  df = dataset1.astype('str')
  dataset = df.values.tolist()
  
  train1 = train.astype('str')
  train = train1.values.tolist()
  
  test1 = test.astype('str')
  test = test1.values.tolist()
  </code></pre>
  
  <p>However, I get the following errors:</p>
  
  <pre><code>Traceback (most recent call last):
    File "GX.py", line 266, in &lt;module&gt;
      scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)
    File "GX.py", line 92, in evaluate_algorithm
      predicted = algorithm(train_set, test_set, *args)
    File "GX.py", line 223, in random_forest
      tree = build_tree(sample, max_depth, min_size, n_features)
    File "GX.py", line 183, in build_tree
      root = get_split(train, n_features)
    File "GX.py", line 137, in get_split
      index = randrange(len(dataset[0]) - 1)
  IndexError: list index out of range
  
  Process finished with exit code 1
  </code></pre>
  
  <p>Here is the code: </p>
  
  <pre><code># Select the best split point for a dataset
  def get_split(dataset, n_features):
      class_values = list(set(row[-1] for row in dataset))
      b_index, b_value, b_score, b_groups = 999, 999, 999, None
      features = list()
      while len(features) &lt; n_features:
          index = randrange(len(dataset[0])-1)
          if index not in features:
              features.append(index)
      for index in features:
          for row in dataset:
              groups = test_split(index, row[index], dataset)
              gini = gini_index(groups, class_values)
              if gini &lt; b_score:
                  b_index, b_value, b_score, b_groups = index, row[index], gini, groups
      return {'index':b_index, 'value':b_value, 'groups':b_groups}
  
  
  # Random Forest Algorithm on Sonar Dataset
  from random import seed
  from random import randrange
  from csv import reader
  from math import sqrt
  
  
  # Load a CSV file
  def load_csv(filename):
      dataset = list()
      with open(filename, 'r') as file:
          csv_reader = reader(file)
          for row in csv_reader:
              if not row:
                  continue
              dataset.append(row)
      return dataset
  
  
  # Convert string column to float
  def str_column_to_float(dataset, column):
      for row in dataset:
          row[column] = float(row[column].strip())
  
  
  # Convert string column to integer
  def str_column_to_int(dataset, column):
      class_values = [row[column] for row in dataset]
      unique = set(class_values)
      lookup = dict()
      for i, value in enumerate(unique):
          lookup[value] = i
      for row in dataset:
          row[column] = lookup[row[column]]
      return lookup
  
  
  # Split a dataset into k folds
  def cross_validation_split(dataset, n_folds):
      dataset_split = list()
      dataset_copy = list(dataset)
      fold_size = int(len(dataset) / n_folds)
      for i in range(n_folds):
          fold = list()
          while len(fold) &lt; fold_size:
              index = randrange(len(dataset_copy))
              fold.append(dataset_copy.pop(index))
          dataset_split.append(fold)
      return dataset_split
  
  
  # Calculate accuracy percentage
  def accuracy_metric(actual, predicted):
      correct = 0
      for i in range(len(actual)):
          if actual[i] == predicted[i]:
              correct += 1
      return correct / float(len(actual)) * 100.0
  
  
  # Evaluate an algorithm using a cross validation split
  def evaluate_algorithm(dataset, algorithm, n_folds, *args):
      folds = cross_validation_split(dataset, n_folds)
      scores = list()
      for fold in folds:
          train_set = list(folds)
          train_set.remove(fold)
          train_set = sum(train_set, [])
          test_set = list()
          for row in fold:
              row_copy = list(row)
              test_set.append(row_copy)
              row_copy[-1] = None
          predicted = algorithm(train_set, test_set, *args)
          actual = [row[-1] for row in fold]
          accuracy = accuracy_metric(actual, predicted)
          scores.append(accuracy)
      return scores
  
  
  # Split a dataset based on an attribute and an attribute value
  def test_split(index, value, dataset):
      left, right = list(), list()
      for row in dataset:
          if row[index] &lt; value:
              left.append(row)
          else:
              right.append(row)
      return left, right
  
  
  # Calculate the Gini index for a split dataset
  def gini_index(groups, classes):
      # count all samples at split point
      n_instances = float(sum([len(group) for group in groups]))
      # sum weighted Gini index for each group
      gini = 0.0
      for group in groups:
          size = float(len(group))
          # avoid divide by zero
          if size == 0:
              continue
          score = 0.0
          # score the group based on the score for each class
          for class_val in classes:
              p = [row[-1] for row in group].count(class_val) / size
              score += p * p
          # weight the group score by its relative size
          gini += (1.0 - score) * (size / n_instances)
      return gini
  
  
  # Select the best split point for a dataset
  def get_split(dataset, n_features):
      class_values = list(set(row[-1] for row in dataset))
      b_index, b_value, b_score, b_groups = 999, 999, 999, None
      features = list()
      while len(features) &lt; n_features:
          index = randrange(len(dataset[0]) - 1)
          if index not in features:
              features.append(index)
      for index in features:
          for row in dataset:
              groups = test_split(index, row[index], dataset)
              gini = gini_index(groups, class_values)
              if gini &lt; b_score:
                  b_index, b_value, b_score, b_groups = index, row[index], gini, groups
      return {'index': b_index, 'value': b_value, 'groups': b_groups}
  
  
  # Create a terminal node value
  def to_terminal(group):
      outcomes = [row[-1] for row in group]
      return max(set(outcomes), key=outcomes.count)
  
  
  # Create child splits for a node or make terminal
  def split(node, max_depth, min_size, n_features, depth):
      left, right = node['groups']
      del (node['groups'])
      # check for a no split
      if not left or not right:
          node['left'] = node['right'] = to_terminal(left + right)
          return
      # check for max depth
      if depth &gt;= max_depth:
          node['left'], node['right'] = to_terminal(left), to_terminal(right)
          return
      # process left child
      if len(left) &lt;= min_size:
          node['left'] = to_terminal(left)
      else:
          node['left'] = get_split(left, n_features)
          split(node['left'], max_depth, min_size, n_features, depth + 1)
      # process right child
      if len(right) &lt;= min_size:
          node['right'] = to_terminal(right)
      else:
          node['right'] = get_split(right, n_features)
          split(node['right'], max_depth, min_size, n_features, depth + 1)
  
  
  # Build a decision tree
  def build_tree(train, max_depth, min_size, n_features):
      root = get_split(train, n_features)
      split(root, max_depth, min_size, n_features, 1)
      return root
  
  
  # Make a prediction with a decision tree
  def predict(node, row):
      if row[node['index']] &lt; node['value']:
          if isinstance(node['left'], dict):
              return predict(node['left'], row)
          else:
              return node['left']
      else:
          if isinstance(node['right'], dict):
              return predict(node['right'], row)
          else:
              return node['right']
  
  
  # Create a random subsample from the dataset with replacement
  def subsample(dataset, ratio):
      sample = list()
      n_sample = round(len(dataset) * ratio)
      while len(sample) &lt; n_sample:
          index = randrange(len(dataset))
          sample.append(dataset[index])
      return sample
  
  
  # Make a prediction with a list of bagged trees
  def bagging_predict(trees, row):
      predictions = [predict(tree, row) for tree in trees]
      return max(set(predictions), key=predictions.count)
  
  
  # Random Forest Algorithm
  def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features):
      trees = list()
      for i in range(n_trees):
          sample = subsample(train, sample_size)
          tree = build_tree(sample, max_depth, min_size, n_features)
          trees.append(tree)
      predictions = [bagging_predict(trees, row) for row in test]
      return (predictions)
  
  seed(1)
  import pandas as pd
  file_path ='https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data'
  dataset2 = pd.read_csv(file_path, header=None, sep=',')
  
  from sklearn.ensemble import RandomForestClassifier
  
  from sklearn.model_selection import train_test_split
  from sklearn import preprocessing
  dataset1 = pd.DataFrame(dataset2)
  dataset1 = dataset1.drop(0, axis=1)
  
  train, test = train_test_split(dataset1, test_size=0.1, random_state = 0) ###&lt;-----
  df = dataset1.astype('str')
  dataset = df.values.tolist()
  
  train1 = train.astype('str')
  train = train1.values.tolist()
  
  test1 = test.astype('str')
  test = test1.values.tolist()
  
  target_index = 0 ##&lt;----
  for i in range(0, len(dataset[0])):
          if i != target_index:
              str_column_to_float(dataset, i)
  # convert class column to integers
  str_column_to_int(dataset, target_index)
  
  # evaluate algorithm
  n_folds = 1
  max_depth = 10
  min_size = 1
  sample_size = 1.0
  n_features = int(sqrt(len(dataset[0]) - 1))
  
  
  for n_trees in [5]:
      scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)
      print('Trees: %d' % n_trees)
      print('Scores: %s' % scores)
      print('Mean Accuracy: %.3f%%' % (sum(scores) / float(len(scores))))
  </code></pre>