title: The output from the .fit_transform method
body: <p>I'd like to have a deeper understanding of what is being output by the method .fit_transform() in the PolynomialFeatures class in scikit learn.</p>
  
  <p>I understand that the method is doing two things, 1) generating a model to the data by fitting it to a regression algorithm, and 2) creating new data based on the model found in 1.</p>
  
  <p>But what I am not understanding is the output. Here is my code:</p>
  
  <pre><code>import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split
  
  
  np.random.seed(0)
  n = 15
  x = np.linspace(0,10,n) + np.random.randn(n)/5
  y = np.sin(x)+x/6 + np.random.randn(n)/10
  
  
  X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0)
  X_train1 = X_train.reshape(11,1)
  y_train1 = y_train.reshape(11,1)
  
  def answer_one():
      from sklearn.linear_model import LinearRegression
      from sklearn.preprocessing import PolynomialFeatures
  
      poly1 = PolynomialFeatures(degree=1)
  
      X_poly1 = poly1.fit_transform(X_train1)
  
      return X_poly1
  
  answer_one()
  </code></pre>
  
  <p>The output I get is:</p>
  
  <pre><code>array([[  1.        ,  10.08877265],
         [  1.        ,   3.23065446],
         [  1.        ,   1.62431903],
         [  1.        ,   9.31004929],
         [  1.        ,   7.17166586],
         [  1.        ,   4.96972856],
         [  1.        ,   8.14799756],
         [  1.        ,   2.59103578],
         [  1.        ,   0.35281047],
         [  1.        ,   3.375973  ],
         [  1.        ,   8.72363612]])
  </code></pre>
  
  <p>I assume each second number in each mini array is a value calculated by the model, but I don't understand what each 1 is?</p>