title: How to predict manually in Random Forest Regressor in Python?
body: <p>I have this Random forest model
  where X_train, X_test as well as y_train, y_test are numpy arrays of shape of (1,n) and (1,m)
  i.e. input consists of only one feature</p>
  <pre class="lang-py prettyprint-override"><code>model_1 = RandomForestRegressor(n_estimators=50,random_state=42)
  model_1.fit(X_train.reshape(-1,1), y_train.reshape(-1,1))
  print(model_1.score(X_test.reshape(-1,1), y_test.reshape(-1,1)))
  </code></pre>
  <p>which works totally fine to fit training data and then also gives a score of around 0.95 respectively for test data but now if I want to predict for</p>
  <pre class="lang-py prettyprint-override"><code>future = np.array([int(i) for i in range(len(X)+1,len(X)+11)])
  </code></pre>
  <p>so future is</p>
  <pre class="lang-py prettyprint-override"><code>array([155, 156, 157, 158, 159, 160, 161, 162, 163, 164])
  </code></pre>
  <p>I did this :</p>
  <pre class="lang-py prettyprint-override"><code>model_1.predict(future.reshape(-1, 1))
  </code></pre>
  <p>But in the output I got all same values</p>
  <pre class="lang-py prettyprint-override"><code>array([2985.02, 2985.02, 2985.02, 2985.02, 2985.02, 2985.02, 2985.02,
         2985.02, 2985.02, 2985.02])
  </code></pre>
  <p>Can somebody tell me why I am getting all predictions to be a same number ?
  and this is just not happening for 10 future values but even for 100 values.
  Is there any other way to predict results manually ?</p>