predicted_value1 = regr1.predict(9) 这句话的意思

predicted_value1 = regr1.predict(9) meaning of this statement

我是数据科学的新手,我已经下载了下周将告诉观众的代码。

但在以下代码中,我无法理解以下函数的作用,以及它将如何预测值。

数据集每个都有 7 个值。为什么大括号只插入了9个?

    regr1 = linear_model.LinearRegression()
    regr1.fit(x1, y1)
    predicted_value1 = regr1.predict(9)

这些线会做什么?

完整代码如下:

   import pandas as pd
   def get_data(file_name):
      data = pd.read_csv(file_name)
      flash_x_parameter = []
      flash_y_parameter = []
      arrow_x_parameter = []
      arrow_y_parameter = []
      for x1,y1,x2,y2 in zip(data['flash_episode_number'], 
          data['flash_us_viewers'],
          data['arrow_episode_number'],data['arrow_us_viewers']):
                   flash_x_parameter.append([float(x1)])
                   flash_y_parameter.append(float(y1))
                   arrow_x_parameter.append([float(x2)])
                   arrow_y_parameter.append(float(y2))
   return flash_x_parameter,
       flash_y_parameter,arrow_x_parameter,arrow_y_parameter


  def more_viewers(x1,y1,x2,y2):
       regr1 = linear_model.LinearRegression()
       regr1.fit(x1, y1)
       predicted_value1 = regr1.predict(9)

      regr2 = linear_model.LinearRegression()
      regr2.fit(x2, y2)
      predicted_value2 = regr2.predict(9)
      print predicted_value1,"are the flash viewers"
      print predicted_value2,"are the arrow viewers"
      if predicted_value1 > predicted_value2:
          print "The Flash Tv Show will have more viewers for next week"
    else:
         print "Arrow Tv Show will have more viewers for next week"
    x1,y1,x2,y2 = get_data('C:\Users\SHIVAPRASAD\Desktop\test.csv')

    more_viewers(x1,y1,x2,y2)

不,您的数据不是 7 值的集合,它有 9 行:

+----------------+-------------------+----------------+------------------+
| FLASH_EPISODE  | FLASH_US_VIEWERS  | ARROW_EPISODE  | ARROW_US_VIEWERS |
+----------------+-------------------+----------------+------------------+
|             1  |             4.83  |             1  |             2.84 |
|             2  |             4.27  |             2  |             2.32 |
|             3  |             3.59  |             3  |             2.55 |
|             4  |             3.53  |             4  |             2.49 |
|             5  |             3.46  |             5  |             2.73 |
|             6  |             3.73  |             6  |              2.6 |
|             7  |             3.47  |             7  |             2.64 |
|             8  |             4.34  |             8  |             3.92 |
|             9  |             4.66  |             9  |             3.06 |
+----------------+-------------------+----------------+------------------+

(因为您的代码来自 Dataconomy Linear Regression Implementation in Python。)

所以命令中的值9

    predicted_value1 = regr1.predict(9)

可以。