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十一、加权线性回归案例：预测鲍鱼的年龄

發布時間：2024/7/5 编程问答 41 豆豆

生活随笔收集整理的這篇文章主要介紹了十一、加权线性回归案例：预测鲍鱼的年龄小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

加權線性回歸案例：預測鮑魚的年齡

點擊文章標題即可獲取源代碼和筆記
數據集：https://download.csdn.net/download/weixin_44827418/12553408

1.導入數據集

數據集描述：

import pandas as pd import numpy as npabalone = pd.read_table("./datas/abalone.txt",header=None) abalone.columns=['性別','長度','直徑','高度','整體重量','肉重量','內臟重量','殼重','年齡'] abalone.head() 性別長度直徑高度整體重量肉重量內臟重量殼重年齡01234

1	0.455	0.365	0.095	0.5140	0.2245	0.1010	0.150	15
1	0.350	0.265	0.090	0.2255	0.0995	0.0485	0.070	7
-1	0.530	0.420	0.135	0.6770	0.2565	0.1415	0.210	9
1	0.440	0.365	0.125	0.5160	0.2155	0.1140	0.155	10
0	0.330	0.255	0.080	0.2050	0.0895	0.0395	0.055	7

abalone.shape (4177, 9) abalone.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 4177 entries, 0 to 4176 Data columns (total 9 columns):# Column Non-Null Count Dtype --- ------ -------------- ----- 0 性別 4177 non-null int64 1 長度 4177 non-null float642 直徑 4177 non-null float643 高度 4177 non-null float644 整體重量 4177 non-null float645 肉重量 4177 non-null float646 內臟重量 4177 non-null float647 殼重 4177 non-null float648 年齡 4177 non-null int64 dtypes: float64(7), int64(2) memory usage: 293.8 KB abalone.describe() 性別長度直徑高度整體重量肉重量內臟重量殼重年齡countmeanstdmin25%50%75%max

4177.000000	4177.000000	4177.000000	4177.000000	4177.000000	4177.000000	4177.000000	4177.000000	4177.000000
0.052909	0.523992	0.407881	0.139516	0.828742	0.359367	0.180594	0.238831	9.933684
0.822240	0.120093	0.099240	0.041827	0.490389	0.221963	0.109614	0.139203	3.224169
-1.000000	0.075000	0.055000	0.000000	0.002000	0.001000	0.000500	0.001500	1.000000
-1.000000	0.450000	0.350000	0.115000	0.441500	0.186000	0.093500	0.130000	8.000000
0.000000	0.545000	0.425000	0.140000	0.799500	0.336000	0.171000	0.234000	9.000000
1.000000	0.615000	0.480000	0.165000	1.153000	0.502000	0.253000	0.329000	11.000000
1.000000	0.815000	0.650000	1.130000	2.825500	1.488000	0.760000	1.005000	29.000000

2. 查看數據分布狀況

import numpy as np import pandas as pd import random import matplotlib as mpl import matplotlib.pyplot as plt plt.rcParams['font.sans-serif']=['simhei'] #顯示中文 plt.rcParams['axes.unicode_minus']=False # 用來正常顯示負號 %matplotlib inline mpl.cm.rainbow(np.linspace(0,1,10)) array([[5.00000000e-01, 0.00000000e+00, 1.00000000e+00, 1.00000000e+00],[2.80392157e-01, 3.38158275e-01, 9.85162233e-01, 1.00000000e+00],[6.07843137e-02, 6.36474236e-01, 9.41089253e-01, 1.00000000e+00],[1.66666667e-01, 8.66025404e-01, 8.66025404e-01, 1.00000000e+00],[3.86274510e-01, 9.84086337e-01, 7.67362681e-01, 1.00000000e+00],[6.13725490e-01, 9.84086337e-01, 6.41213315e-01, 1.00000000e+00],[8.33333333e-01, 8.66025404e-01, 5.00000000e-01, 1.00000000e+00],[1.00000000e+00, 6.36474236e-01, 3.38158275e-01, 1.00000000e+00],[1.00000000e+00, 3.38158275e-01, 1.71625679e-01, 1.00000000e+00],[1.00000000e+00, 1.22464680e-16, 6.12323400e-17, 1.00000000e+00]]) mpl.cm.rainbow(np.linspace(0,1,10))[0] array([0.5, 0. , 1. , 1. ]) def dataPlot(dataSet):m,n = dataSet.shapefig = plt.figure(figsize=(8,20),dpi=100)colormap = mpl.cm.rainbow(np.linspace(0,1,n))for i in range(n):fig_ = fig.add_subplot(n,1,i+1)plt.scatter(range(m),dataSet.iloc[:,i].values,s=2,c=colormap[i])plt.title(dataSet.columns[i])plt.tight_layout(pad=1.2) # 調節子圖間的距離 # 運行函數，查看數據分布： dataPlot(abalone) 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.

可以從數據分布散點圖中看出：

1）除“性別”之外，其他數據明顯存在規律性排列

2）“高度”這一特征中，有兩個異常值

從看到的現象，我們可以采取以下兩種措施：

1）切分訓練集和測試集時，需要打亂原始數據集來進行隨機挑選

2）剔除"高度"這一特征中的異常值

abalone['高度']<0.4 0 True 1 True 2 True 3 True 4 True... 4172 True 4173 True 4174 True 4175 True 4176 True Name: 高度, Length: 4177, dtype: bool aba = abalone.loc[abalone['高度']<0.4,:] #再次查看數據集的分布 dataPlot(aba) 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.

2. 切分訓練集和測試集

""" 函數功能：隨機切分訓練集和測試集參數說明：dataSet：原始數據集rate:訓練集比例返回：train,test：切分好的訓練集和測試集 """ def randSplit(dataSet,rate):l = list(dataSet.index) # 將原始數據集的索引提取出來，存到列表中random.seed(123) # 設置隨機數種子random.shuffle(l) # 隨機打亂數據集中的索引dataSet.index = l # 把打亂后的索引重新賦值給數據集中的索引，# 索引打亂了就相當于打亂了原始數據集中的數據m = dataSet.shape[0] # 原始數據集樣本總數n = int(m*rate) # 訓練集樣本數量train = dataSet.loc[range(n),:] # 從打亂了的原始數據集中提取出訓練集數據test = dataSet.loc[range(n,m),:] # 從打亂了的原始數據集中提取出測試集數據train.index = range(train.shape[0]) # 重置train訓練數據集中的索引test.index = range(test.shape[0]) # 重置test測試數據集中的索引dataSet.index = range(dataSet.shape[0]) # 重置原始數據集中的索引return train,test train,test = randSplit(aba,0.8) #探索訓練集 train.head() 性別長度直徑高度整體重量肉重量內臟重量殼重年齡01234

-1	0.590	0.470	0.170	0.9000	0.3550	0.1905	0.2500	11
1	0.560	0.450	0.145	0.9355	0.4250	0.1645	0.2725	11
-1	0.635	0.535	0.190	1.2420	0.5760	0.2475	0.3900	14
1	0.505	0.390	0.115	0.5585	0.2575	0.1190	0.1535	8
1	0.510	0.410	0.145	0.7960	0.3865	0.1815	0.1955	8

train.shape (3340, 9) abalone.describe() 性別長度直徑高度整體重量肉重量內臟重量殼重年齡countmeanstdmin25%50%75%max

4177.000000	4177.000000	4177.000000	4177.000000	4177.000000	4177.000000	4177.000000	4177.000000	4177.000000
0.052909	0.523992	0.407881	0.139516	0.828742	0.359367	0.180594	0.238831	9.933684
0.822240	0.120093	0.099240	0.041827	0.490389	0.221963	0.109614	0.139203	3.224169
-1.000000	0.075000	0.055000	0.000000	0.002000	0.001000	0.000500	0.001500	1.000000
-1.000000	0.450000	0.350000	0.115000	0.441500	0.186000	0.093500	0.130000	8.000000
0.000000	0.545000	0.425000	0.140000	0.799500	0.336000	0.171000	0.234000	9.000000
1.000000	0.615000	0.480000	0.165000	1.153000	0.502000	0.253000	0.329000	11.000000
1.000000	0.815000	0.650000	1.130000	2.825500	1.488000	0.760000	1.005000	29.000000

train.describe() #統計描述性別長度直徑高度整體重量肉重量內臟重量殼重年齡countmeanstdmin25%50%75%max

3340.000000	3340.000000	3340.000000	3340.000000	3340.000000	3340.000000	3340.000000	3340.000000	3340.000000
0.060479	0.522754	0.406886	0.138790	0.824906	0.358151	0.179732	0.237158	9.911976
0.819021	0.120300	0.099372	0.038441	0.488535	0.222422	0.109036	0.137920	3.223534
-1.000000	0.075000	0.055000	0.000000	0.002000	0.001000	0.000500	0.001500	1.000000
-1.000000	0.450000	0.350000	0.115000	0.439000	0.184375	0.092000	0.130000	8.000000
0.000000	0.540000	0.420000	0.140000	0.796750	0.335500	0.171000	0.232000	9.000000
1.000000	0.615000	0.480000	0.165000	1.147250	0.498500	0.250500	0.325000	11.000000
1.000000	0.780000	0.630000	0.250000	2.825500	1.488000	0.760000	1.005000	27.000000

dataPlot(train) #查看訓練集數據分布 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.

#探索測試集 test.head() 性別長度直徑高度整體重量肉重量內臟重量殼重年齡01234

1	0.630	0.470	0.150	1.1355	0.5390	0.2325	0.3115	12
-1	0.585	0.445	0.140	0.9130	0.4305	0.2205	0.2530	10
-1	0.390	0.290	0.125	0.3055	0.1210	0.0820	0.0900	7
1	0.525	0.410	0.130	0.9900	0.3865	0.2430	0.2950	15
1	0.625	0.475	0.160	1.0845	0.5005	0.2355	0.3105	10

test.shape (835, 9) test.describe() 性別長度直徑高度整體重量肉重量內臟重量殼重年齡countmeanstdmin25%50%75%max

835.000000	835.000000	835.000000	835.000000	835.000000	835.000000	835.000000	835.000000	835.000000
0.022754	0.528808	0.411737	0.140784	0.842714	0.363370	0.183749	0.245320	10.022754
0.834341	0.119166	0.098627	0.038664	0.495990	0.218938	0.111510	0.143925	3.230284
-1.000000	0.130000	0.100000	0.015000	0.013000	0.004500	0.003000	0.004000	3.000000
-1.000000	0.450000	0.350000	0.115000	0.458000	0.192000	0.096500	0.132750	8.000000
0.000000	0.550000	0.430000	0.140000	0.810000	0.339000	0.170500	0.235000	10.000000
1.000000	0.620000	0.485000	0.170000	1.177250	0.510750	0.259250	0.337000	11.000000
1.000000	0.815000	0.650000	0.250000	2.555000	1.145500	0.590000	0.815000	29.000000

dataPlot(test) 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points. 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.

3.構建輔助函數

''' 函數功能：輸入DF數據集（最后一列為標簽），返回特征矩陣和標簽矩陣 ''' def get_Mat(dataSet):xMat = np.mat(dataSet.iloc[:,:-1].values)yMat = np.mat(dataSet.iloc[:,-1].values).Treturn xMat,yMat ''' 函數功能：數據集可視化 ''' def plotShow(dataSet):xMat,yMat = get_Mat(dataSet)plt.scatter(xMat.A[:,1],yMat.A,c='b',s=5)plt.show() ''' 函數功能：計算回歸系數參數說明：dataSet:原始數據集返回：ws:回歸系數 ''' def standRegres(dataSet):xMat,yMat = get_Mat(dataSet)xTx = xMat.T * xMatif np.linalg.det(xTx) == 0:print('矩陣為奇異矩陣，無法求逆！')returnws = xTx.I*(xMat.T*yMat) # xTx.I ，用來求逆矩陣return ws """ 函數功能：計算誤差平方和SSE 參數說明：dataSet：真實值regres：求回歸系數的函數返回：SSE：誤差平方和 """ def sseCal(dataSet, regres):xMat,yMat = get_Mat(dataSet)ws = regres(dataSet)yHat = xMat*wssse = ((yMat.A.flatten() - yHat.A.flatten())**2).sum()# return sse

以ex0數據集為例，查看函數運行結果：

ex0 = pd.read_table("./datas/ex0.txt",header=None) ex0.head() 01201234

1.0	0.067732	3.176513
1.0	0.427810	3.816464
1.0	0.995731	4.550095
1.0	0.738336	4.256571
1.0	0.981083	4.560815

#簡單線性回歸的SSE sseCal(ex0, standRegres) 1.3552490816814902

構建相關系數R2計算函數

""" 函數功能：計算相關系數R2 """ def rSquare(dataSet,regres):xMat,yMat=get_Mat(dataSet)sse = sseCal(dataSet,regres)sst = ((yMat.A-yMat.mean())**2).sum()# r2 = 1 - sse / sstreturn r2

同樣以ex0數據集為例，查看函數運行結果：

#簡單線性回歸的R2 rSquare(ex0, standRegres) 0.9731300889856916 ''' 函數功能：計算局部加權線性回歸的預測值參數說明：testMat:測試集xMat:訓練集的特征矩陣yMat:訓練集的標簽矩陣返回：yHat：函數預測值 ''' def LWLR(testMat,xMat,yMat,k=1.0):n = testMat.shape[0] # 測試數據集行數m = xMat.shape[0] # 訓練集特征矩陣行數weights = np.mat(np.eye(m)) # 用單位矩陣來初始化權重矩陣，yHat = np.zeros(n) # 用0矩陣來初始化預測值矩陣for i in range(n):for j in range(m):diffMat = testMat[i] - xMat[j]weights[j,j] = np.exp(diffMat*diffMat.T / (-2*k**2))xTx = xMat.T*(weights*xMat)if np.linalg.det(xTx) == 0:print('矩陣為奇異矩陣，無法求逆')returnws = xTx.I*(xMat.T*(weights*yMat))yHat[i] = testMat[i] * wsreturn ws,yHat

4.構建加權線性模型

因為數據量太大，計算速度極慢，所以此處選擇訓練集的前100個數據作為訓練集，測試集的前100個數據作為測試集。

""" 函數功能：繪制不同k取值下，訓練集和測試集的SSE曲線 """ def ssePlot(train,test):X0,Y0 = get_Mat(train)X1,Y1 =get_Mat(test)train_sse = []test_sse = []for k in np.arange(0.2,10,0.5):ws1,yHat1 = LWLR(X0[:99],X0[:99],Y0[:99],k) sse1 = ((Y0[:99].A.T - yHat1)**2).sum() train_sse.append(sse1)ws2,yHat2 = LWLR(X1[:99],X0[:99],Y0[:99],k) sse2 = ((Y1[:99].A.T - yHat2)**2).sum() test_sse.append(sse2)plt.figure(figsize=(20,8),dpi=100)plt.plot(np.arange(0.2,10,0.5),train_sse,color='b')# plt.plot(np.arange(0.2,10,0.5),test_sse,color='r') plt.xlabel('不同k取值')plt.ylabel('SSE')plt.legend(['train_sse','test_sse'])

運行結果：

ssePlot(train,test)

這個圖的解讀應該是這樣的：從右往左看，當K取較大值時，模型比較穩定，隨著K值的減小，訓練集的SSE開始逐漸減小，當K取到2左右，訓練集的SSE與測試集的SSE相等，當K繼續減小時，訓練集的SSE也越來越小，也就是說，模型在訓練集上的表現越來越好，但是，模型在測試集上的表現卻越來越差了，這就說明模型開始出現過擬合了。其實，這個圖與前面不同k值的結果圖是吻合的，K=1.0,
0.01, 0.003這三張圖也表明隨著K的減小，模型會逐漸出現過擬合。所以這里可以看出，K在2左右的取值最佳。

我們再將K=2帶入局部線性回歸模型中，然后查看預測結果：

train,test = randSplit(aba,0.8) # 隨機切分原始數據集，得到訓練集和測試集 trainX,trainY = get_Mat(train) # 將切分好的訓練集分成特征矩陣和標簽矩陣 testX,testY = get_Mat(test) # 將切分好的測試集分成特征矩陣和標簽矩陣 ws0,yHat0 = LWLR(testX,trainX,trainY,k=2)

繪制真實值與預測值之間的關系圖

y=testY.A.flatten() plt.scatter(y,yHat0,c='b',s=5); # ;等效于plt.show()

通過上圖可知，橫坐標為真實值，縱坐標為預測值，形成的圖像為呈現一個“喇叭形”，隨著橫坐標真實值逐漸變大，縱坐標預測值也越來越大，說明隨著真實值的增加，預測值偏差越來越大

封裝一個函數來計算SSE和R方，方便后續調用

""" 函數功能：計算加權線性回歸的SSE和R方 """ def LWLR_pre(dataSet):train,test = randSplit(dataSet,0.8)# trainX,trainY = get_Mat(train)testX,testY = get_Mat(test)ws,yHat = LWLR(testX,trainX,trainY,k=2)# sse = ((testY.A.T - yHat)**2).sum()# sst = ((testY.A-testY.mean())**2).sum() # r2 = 1 - sse / sstreturn sse,r2

查看模型預測結果

LWLR_pre(aba) (4152.777097646255, 0.5228101340130846)

從結果可以看出，SSE達4000+，相關系數只有0.52，模型效果并不是很好。

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