sklearn學習筆記：線性迴歸預測

1. 獲取數據。sklearn中自帶一些經常使用的數據集，點擊打開連接，例如用於迴歸分析的波士頓房價數據集（Boston）、用於分類的鳶尾花數據集（iris）等。現選用Boston數據集，能夠先調用shape()等對數據集的基本狀況進行查看：

from sklearn import datasets
from numpy import shape
loaded_data = datasets.load_boston()
data_X = loaded_data.data
data_y = loaded_data.target
print(shape(data_X))
print(shape(data_y))
print(data_X[:2, :])
print(data_y[:2])

輸出結果：

(506, 13)
(506,)
[[  6.32000000e-03   1.80000000e+01   2.31000000e+00   0.00000000e+00
    5.38000000e-01   6.57500000e+00   6.52000000e+01   4.09000000e+00
    1.00000000e+00   2.96000000e+02   1.53000000e+01   3.96900000e+02
    4.98000000e+00]
 [  2.73100000e-02   0.00000000e+00   7.07000000e+00   0.00000000e+00
    4.69000000e-01   6.42100000e+00   7.89000000e+01   4.96710000e+00
    2.00000000e+00   2.42000000e+02   1.78000000e+01   3.96900000e+02
    9.14000000e+00]]
[ 24.   21.6]

說明該數據集包括506個樣本，每一個樣本有13個特徵值，標籤值爲房價，同時輸出了前兩個樣本的具體狀況。

2.劃分訓練集和測試集。咱們將20%的樣本劃分爲測試集，80%爲訓練集，即test_size=0.2，一樣咱們也能夠調用shape()來查看劃分結果：

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(data_X, data_y, test_size=0.2)
print(shape(X_train))
print(shape(X_test))

輸出結果：

(404, 13)
(102, 13)

3.運行線性模型。咱們選用sklearn中基於最小二乘的線性迴歸模型，並用訓練集進行擬合，獲得擬合直線y=wTx+b中的權重參數w和b：

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
print (model.coef_)
print (model.intercept_)

輸出結果：

[ -1.04864717e-01   3.97233700e-02   1.98757774e-02   2.30896040e+00
  -1.76253192e+01   3.74803039e+00   1.28555952e-04  -1.56689014e+00
   2.97635772e-01  -1.18908274e-02  -9.15199442e-01   1.04446613e-02
  -5.55228840e-01]
36.3527413723

4.模型測試。利用測試集獲得對應的結果，並利用均方根偏差（MSE）對測試結果進行評價：

y_pred = model.predict(X_test)
from sklearn import metrics
print "MSE:", metrics.mean_squared_error(y_test, y_pred)

輸出結果：

MSE: 19.1283413297

5.交叉驗證。咱們使用10折交叉驗證，即cv=10，並求出交叉驗證獲得的MSE值

from sklearn.model_selection import cross_val_predict
predicted = cross_val_predict(model, data_X, data_y, cv=10)
print  "MSE:", metrics.mean_squared_error(data_y, predicted)

輸出結果：

MSE: 34.5970425577

6.畫圖。將實際房價數據與預測數據做出對比，接近中間綠色直線的數據表示預測準確：

import matplotlib.pyplot as plt
plt.scatter(data_y, predicted, color='y', marker='o')
plt.scatter(data_y, data_y,color='g', marker='+')
plt.show()

輸出圖像：

7.完整代碼爲：
 

# -*- encoding:utf-8 -*-

from sklearn import datasets
from sklearn.model_selection import train_test_split #原文中cross_validation已過期改成model_selection
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_predict
from numpy import shape
 
loaded_data = datasets.load_boston()
data_X = loaded_data.data
data_y = loaded_data.target
# print(shape(data_X))
# print(shape(data_y))
# print(data_X[:2, :])
# print(data_y[:2])
 
X_train, X_test, y_train, y_test = train_test_split(data_X, data_y, test_size=0.2)
# print(shape(X_train))
# print shape(X_test)
 
model = LinearRegression()
model.fit(X_train, y_train)
# print (model.coef_)
# print (model.intercept_)
 
y_pred = model.predict(X_test)
from sklearn import metrics
print "MSE:", metrics.mean_squared_error(y_test, y_pred)
 
predicted = cross_val_predict(model, data_X, data_y, cv=10)
print  "MSE:", metrics.mean_squared_error(data_y, predicted)
 
plt.scatter(data_y, predicted, color='y', marker='o')
plt.scatter(data_y, data_y,color='g', marker='+')
plt.show()