title: Why different random_states in ML model?
body: <p>I recently read that specifying a number for the random_state ensures to get the same results in each run.</p>
  <p>Why do I use then random_state=1 when splitting the data into training and validation sets but random_state=0 for creating the model?</p>
  <p>I would have expected them to be both the same value.</p>
  <pre><code>import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import RandomForestClassifier
  
  data = pd.read_csv('../input/fifa-2018-match-statistics/FIFA 2018 Statistics.csv')
  y = (data['Man of the Match'] == &quot;Yes&quot;)  # Convert from string &quot;Yes&quot;/&quot;No&quot; to binary
  feature_names = [i for i in data.columns if data[i].dtype in [np.int64]]
  X = data[feature_names]
  train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
  my_model = RandomForestClassifier(n_estimators=100,
                                    random_state=0).fit(train_X, train_y)
  </code></pre>