邏輯回歸--數據獨熱編碼＋數據結果可視化
阿新 • • 發佈：2018-03-10
ati values group 歸一化 fix sco value space AD
#-*- coding: utf-8 -*- ‘‘‘ 在數據處理和特征工程中，經常會遇到類型數據，如性別分為[男，女]（暫不考慮其他。。。。），手機運營商分為[移動，聯通，電信]等，我們通常將其轉為數值帶入模型，如[0,1], [-1,0,1]等，但模型往往默認為連續型數值進行處理. 獨熱編碼便是解決這個問題，其方法是使用N位狀態寄存器來對N個狀態進行編碼，每個狀態都由他獨立的寄存器位，並且在任意時候，其中只有一位有效。 可以理解為對有m個取值的特征，經過獨熱編碼處理後，轉為m個二元特征(值只有０和１)，每次只有一個激活。 基於樹的方法是不需要進行特征的歸一化，例如隨機森林，bagging 和 boosting等。基於參數的模型或基於距離的模型，都是要進行特征的歸一化。 @author: soyo‘‘‘ import pandas as pd import numpy import matplotlib.pylab as pl from sklearn.linear_model import LogisticRegression from sklearn.cross_validation import train_test_split from sklearn.utils.extmath import cartesian #numpy.set_printoptions(threshold=numpy.inf) #目的是將print省略的部分都輸出 data=pd.read_csv("/home/soyo/文檔/LogisticRegression.csv") print data print data.head(5) data_dum=pd.get_dummies(data,prefix=‘rank‘,columns=[‘rank‘],drop_first=True) #類別型變量進行獨熱編碼，drop_first=True：刪掉了本該有的rank_1 print data_dum.head(5) print "*********************" print data_dum.ix[:,1:].head(5) print data_dum.ix[:,0].head(5) x_train,x_test,y_train,y_test=train_test_split(data_dum.ix[:,1:],data_dum.ix[:,0],test_size=0.1,random_state=1) #x:代表的是數據特征，y:代表的是類標(lable)，都被隨機的拆分開做交叉驗證 print len(x_train),len(x_test) print x_train numpy.savetxt(‘/home/soyo/文檔/new.csv‘, x_train,fmt="%d", delimiter = ‘,‘) print len(y_train),len(y_test) numpy.savetxt(‘/home/soyo/文檔/new2.csv‘, y_train,fmt="%d", delimiter = ‘,‘) print y_train print "***********" print y_test lr=LogisticRegression() lr.fit(x_train,y_train) print "預測結果：" print lr.predict(x_test) print "真實label:" print numpy.array(y_test) print "邏輯回歸的準確率為:{0:.3f}%".format(lr.score(x_test, y_test)) print "根據組合數據分析數據之間的關系" gres=numpy.linspace(data[‘gre‘].min(),data[‘gre‘].max(),20) print gres gpas=numpy.linspace(data[‘gpa‘].min(),data[‘gpa‘].max(),20) print gpas # numpy.set_printoptions(threshold=numpy.inf) #目的是將print省略的部分都輸出 print cartesian([gres,gpas,[1,2,3,4],[1.]]) #數據組合：組合後的總個數－>20*20*4*1=1600個 data_new=pd.DataFrame(cartesian([gres,gpas,[1,2,3,4],[1.]])) print data_new data_new.columns=[‘gre‘,‘gpa‘,‘ranks‘,‘intercept‘] print data_new dummy_ranks=pd.get_dummies(data_new[‘ranks‘],prefix=‘ranks‘) #prefix:前綴名 # print dummy_ranks dummy_ranks.columns=[‘ranks_1‘,‘ranks_2‘,‘ranks_3‘,‘ranks_4‘] print dummy_ranks cols_to_keep=[‘gre‘,‘gpa‘] cobs=data_new[cols_to_keep].join(dummy_ranks.ix[:,‘ranks_2‘:]) print "*********6" print cobs print lr.predict(cobs) data_new[‘predict_admit‘]=lr.predict(cobs) # data_new[‘predict_admit‘]=numpy.linspace(5,100,1600) print data_new grouped=pd.pivot_table(data_new,values=[‘predict_admit‘],index=[‘gre‘,‘ranks‘],aggfunc=numpy.mean) print grouped print "*********9" print grouped.index.get_level_values(1) print grouped.ix[grouped.index.get_level_values(1)==2].index.get_level_values(0) def target_plot(x): grouped=pd.pivot_table(data_new,values=[‘predict_admit‘],index=[x,‘ranks‘],aggfunc=numpy.mean) #pivot_table:數據透視表－>為了聚合統計數據 colors=‘rbgyrbgy‘ for col in data_new.ranks.unique(): plt_data=grouped.ix[grouped.index.get_level_values(1)==col] pl.plot(plt_data.index.get_level_values(0),plt_data[‘predict_admit‘],color=colors[int(col)]) pl.xlabel(x) pl.ylabel("P(admit=1") pl.legend([‘1‘,‘2‘,‘3‘,‘4‘],loc=‘upper left‘,title=‘ranks‘) pl.title("soyo") pl.show() target_plot(‘gpa‘)
結果：
admit gre gpa rank 0 0 380 3.61 3 1 1 660 3.67 3 2 1 800 4.00 1 3 1 640 3.19 4 4 0 520 2.93 4 5 1 760 3.00 2 6 1 560 2.98 1 7 0 400 3.08 2 8 1 540 3.39 3 9 0 700 3.92 2 10 0 800 4.00 4 11 0 440 3.22 1 12 1 760 4.00 1 13 0 700 3.08 2 14 1 700 4.00 1 15 0 480 3.44 3 16 0 780 3.87 4 17 0 360 2.56 3 18 0 800 3.75 2 19 1 540 3.81 1 20 0 500 3.17 3 21 1 660 3.63 2 22 0 600 2.82 4 23 0 680 3.19 4 24 1 760 3.35 2 25 1 800 3.66 1 26 1 620 3.61 1 27 1 520 3.74 4 28 1 780 3.22 2 29 0 520 3.29 1 .. ... ... ... ... 370 1 540 3.77 2 371 1 680 3.76 3 372 1 680 2.42 1 373 1 620 3.37 1 374 0 560 3.78 2 375 0 560 3.49 4 376 0 620 3.63 2 377 1 800 4.00 2 378 0 640 3.12 3 379 0 540 2.70 2 380 0 700 3.65 2 381 1 540 3.49 2 382 0 540 3.51 2 383 0 660 4.00 1 384 1 480 2.62 2 385 0 420 3.02 1 386 1 740 3.86 2 387 0 580 3.36 2 388 0 640 3.17 2 389 0 640 3.51 2 390 1 800 3.05 2 391 1 660 3.88 2 392 1 600 3.38 3 393 1 620 3.75 2 394 1 460 3.99 3 395 0 620 4.00 2 396 0 560 3.04 3 397 0 460 2.63 2 398 0 700 3.65 2 399 0 600 3.89 3 [400 rows x 4 columns] admit gre gpa rank 0 0 380 3.61 3 1 1 660 3.67 3 2 1 800 4.00 1 3 1 640 3.19 4 4 0 520 2.93 4 admit gre gpa rank_2 rank_3 rank_4 0 0 380 3.61 0.0 1.0 0.0 1 1 660 3.67 0.0 1.0 0.0 2 1 800 4.00 0.0 0.0 0.0 3 1 640 3.19 0.0 0.0 1.0 4 0 520 2.93 0.0 0.0 1.0 ********************* gre gpa rank_2 rank_3 rank_4 0 380 3.61 0.0 1.0 0.0 1 660 3.67 0.0 1.0 0.0 2 800 4.00 0.0 0.0 0.0 3 640 3.19 0.0 0.0 1.0 4 520 2.93 0.0 0.0 1.0 0 0 1 1 2 1 3 1 4 0 Name: admit, dtype: int64 360 40 gre gpa rank_2 rank_3 rank_4 268 680 3.46 1.0 0.0 0.0 204 600 3.89 0.0 0.0 0.0 171 540 2.81 0.0 1.0 0.0 62 640 3.67 0.0 1.0 0.0 385 420 3.02 0.0 0.0 0.0 85 520 2.98 1.0 0.0 0.0 389 640 3.51 1.0 0.0 0.0 307 580 3.51 1.0 0.0 0.0 314 540 3.46 0.0 0.0 1.0 278 680 3.00 0.0 0.0 1.0 65 600 3.59 1.0 0.0 0.0 225 720 3.50 0.0 1.0 0.0 229 720 3.42 1.0 0.0 0.0 18 800 3.75 1.0 0.0 0.0 296 560 3.16 0.0 0.0 0.0 286 800 3.22 0.0 0.0 0.0 272 680 3.67 1.0 0.0 0.0 117 700 3.72 1.0 0.0 0.0 258 520 3.51 1.0 0.0 0.0 360 520 4.00 0.0 0.0 0.0 107 480 3.13 1.0 0.0 0.0 67 620 3.30 0.0 0.0 0.0 234 800 3.53 0.0 0.0 0.0 246 680 3.34 1.0 0.0 0.0 354 540 3.78 1.0 0.0 0.0 222 480 3.02 0.0 0.0 0.0 106 700 3.56 0.0 0.0 0.0 310 560 4.00 0.0 1.0 0.0 270 640 3.95 1.0 0.0 0.0 312 660 3.77 0.0 1.0 0.0 .. ... ... ... ... ... 317 780 3.63 0.0 0.0 1.0 319 540 3.28 0.0 0.0 0.0 7 400 3.08 1.0 0.0 0.0 141 700 3.52 0.0 0.0 1.0 86 600 3.32 1.0 0.0 0.0 352 580 3.12 0.0 1.0 0.0 241 520 3.81 0.0 0.0 0.0 215 660 2.91 0.0 1.0 0.0 68 580 3.69 0.0 0.0 0.0 50 640 3.86 0.0 1.0 0.0 156 560 2.52 1.0 0.0 0.0 252 520 4.00 1.0 0.0 0.0 357 720 3.31 0.0 0.0 0.0 254 740 3.52 0.0 0.0 1.0 276 460 3.77 0.0 1.0 0.0 178 620 3.33 0.0 1.0 0.0 281 360 3.27 0.0 1.0 0.0 237 480 4.00 1.0 0.0 0.0 71 300 2.92 0.0 0.0 1.0 129 460 3.15 0.0 0.0 1.0 144 580 3.40 0.0 0.0 1.0 335 620 3.71 0.0 0.0 0.0 133 500 3.08 0.0 1.0 0.0 203 420 3.92 0.0 0.0 1.0 393 620 3.75 1.0 0.0 0.0 255 640 3.35 0.0 1.0 0.0 72 480 3.39 0.0 0.0 1.0 396 560 3.04 0.0 1.0 0.0 235 620 3.05 1.0 0.0 0.0 37 520 2.90 0.0 1.0 0.0 [360 rows x 5 columns] 360 40 268 1 204 1 171 0 62 0 385 0 85 0 389 0 307 0 314 0 278 1 65 0 225 1 229 1 18 0 296 0 286 1 272 1 117 0 258 0 360 1 107 0 67 0 234 1 246 0 354 1 222 1 106 1 310 0 270 1 312 0 .. 317 1 319 0 7 0 141 1 86 0 352 1 241 1 215 1 68 0 50 0 156 0 252 1 357 0 254 1 276 0 178 0 281 0 237 0 71 0 129 0 144 0 335 1 133 0 203 0 393 1 255 0 72 0 396 0 235 0 37 0 Name: admit, dtype: int64 *********** 398 0 125 0 328 0 339 1 172 0 342 0 197 1 291 0 29 0 284 1 174 0 372 1 188 0 324 0 321 0 227 0 371 1 5 1 78 0 223 0 122 0 242 1 382 0 214 1 17 0 92 0 366 0 201 1 361 1 207 1 81 0 4 0 165 0 275 1 6 1 80 0 58 0 102 0 397 0 139 1 Name: admit, dtype: int64 預測結果： [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1] 真實label: [0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 1] 邏輯回歸的準確率為:0.675% 根據組合數據分析數據之間的關系 [ 220. 250.52631579 281.05263158 311.57894737 342.10526316 372.63157895 403.15789474 433.68421053 464.21052632 494.73684211 525.26315789 555.78947368 586.31578947 616.84210526 647.36842105 677.89473684 708.42105263 738.94736842 769.47368421 800. ] [ 2.26 2.35157895 2.44315789 2.53473684 2.62631579 2.71789474 2.80947368 2.90105263 2.99263158 3.08421053 3.17578947 3.26736842 3.35894737 3.45052632 3.54210526 3.63368421 3.72526316 3.81684211 3.90842105 4. ] [[ 220. 2.26 1. 1. ] [ 220. 2.26 2. 1. ] [ 220. 2.26 3. 1. ] ..., [ 800. 4. 2. 1. ] [ 800. 4. 3. 1. ] [ 800. 4. 4. 1. ]] 0 1 2 3 0 220.0 2.260000 1.0 1.0 1 220.0 2.260000 2.0 1.0 2 220.0 2.260000 3.0 1.0 3 220.0 2.260000 4.0 1.0 4 220.0 2.351579 1.0 1.0 5 220.0 2.351579 2.0 1.0 6 220.0 2.351579 3.0 1.0 7 220.0 2.351579 4.0 1.0 8 220.0 2.443158 1.0 1.0 9 220.0 2.443158 2.0 1.0 10 220.0 2.443158 3.0 1.0 11 220.0 2.443158 4.0 1.0 12 220.0 2.534737 1.0 1.0 13 220.0 2.534737 2.0 1.0 14 220.0 2.534737 3.0 1.0 15 220.0 2.534737 4.0 1.0 16 220.0 2.626316 1.0 1.0 17 220.0 2.626316 2.0 1.0 18 220.0 2.626316 3.0 1.0 19 220.0 2.626316 4.0 1.0 20 220.0 2.717895 1.0 1.0 21 220.0 2.717895 2.0 1.0 22 220.0 2.717895 3.0 1.0 23 220.0 2.717895 4.0 1.0 24 220.0 2.809474 1.0 1.0 25 220.0 2.809474 2.0 1.0 26 220.0 2.809474 3.0 1.0 27 220.0 2.809474 4.0 1.0 28 220.0 2.901053 1.0 1.0 29 220.0 2.901053 2.0 1.0 ... ... ... ... ... 1570 800.0 3.358947 3.0 1.0 1571 800.0 3.358947 4.0 1.0 1572 800.0 3.450526 1.0 1.0 1573 800.0 3.450526 2.0 1.0 1574 800.0 3.450526 3.0 1.0 1575 800.0 3.450526 4.0 1.0 1576 800.0 3.542105 1.0 1.0 1577 800.0 3.542105 2.0 1.0 1578 800.0 3.542105 3.0 1.0 1579 800.0 3.542105 4.0 1.0 1580 800.0 3.633684 1.0 1.0 1581 800.0 3.633684 2.0 1.0 1582 800.0 3.633684 3.0 1.0 1583 800.0 3.633684 4.0 1.0 1584 800.0 3.725263 1.0 1.0 1585 800.0 3.725263 2.0 1.0 1586 800.0 3.725263 3.0 1.0 1587 800.0 3.725263 4.0 1.0 1588 800.0 3.816842 1.0 1.0 1589 800.0 3.816842 2.0 1.0 1590 800.0 3.816842 3.0 1.0 1591 800.0 3.816842 4.0 1.0 1592 800.0 3.908421 1.0 1.0 1593 800.0 3.908421 2.0 1.0 1594 800.0 3.908421 3.0 1.0 1595 800.0 3.908421 4.0 1.0 1596 800.0 4.000000 1.0 1.0 1597 800.0 4.000000 2.0 1.0 1598 800.0 4.000000 3.0 1.0 1599 800.0 4.000000 4.0 1.0 [1600 rows x 4 columns] gre gpa ranks intercept 0 220.0 2.260000 1.0 1.0 1 220.0 2.260000 2.0 1.0 2 220.0 2.260000 3.0 1.0 3 220.0 2.260000 4.0 1.0 4 220.0 2.351579 1.0 1.0 5 220.0 2.351579 2.0 1.0 6 220.0 2.351579 3.0 1.0 7 220.0 2.351579 4.0 1.0 8 220.0 2.443158 1.0 1.0 9 220.0 2.443158 2.0 1.0 10 220.0 2.443158 3.0 1.0 11 220.0 2.443158 4.0 1.0 12 220.0 2.534737 1.0 1.0 13 220.0 2.534737 2.0 1.0 14 220.0 2.534737 3.0 1.0 15 220.0 2.534737 4.0 1.0 16 220.0 2.626316 1.0 1.0 17 220.0 2.626316 2.0 1.0 18 220.0 2.626316 3.0 1.0 19 220.0 2.626316 4.0 1.0 20 220.0 2.717895 1.0 1.0 21 220.0 2.717895 2.0 1.0 22 220.0 2.717895 3.0 1.0 23 220.0 2.717895 4.0 1.0 24 220.0 2.809474 1.0 1.0 25 220.0 2.809474 2.0 1.0 26 220.0 2.809474 3.0 1.0 27 220.0 2.809474 4.0 1.0 28 220.0 2.901053 1.0 1.0 29 220.0 2.901053 2.0 1.0 ... ... ... ... ... 1570 800.0 3.358947 3.0 1.0 1571 800.0 3.358947 4.0 1.0 1572 800.0 3.450526 1.0 1.0 1573 800.0 3.450526 2.0 1.0 1574 800.0 3.450526 3.0 1.0 1575 800.0 3.450526 4.0 1.0 1576 800.0 3.542105 1.0 1.0 1577 800.0 3.542105 2.0 1.0 1578 800.0 3.542105 3.0 1.0 1579 800.0 3.542105 4.0 1.0 1580 800.0 3.633684 1.0 1.0 1581 800.0 3.633684 2.0 1.0 1582 800.0 3.633684 3.0 1.0 1583 800.0 3.633684 4.0 1.0 1584 800.0 3.725263 1.0 1.0 1585 800.0 3.725263 2.0 1.0 1586 800.0 3.725263 3.0 1.0 1587 800.0 3.725263 4.0 1.0 1588 800.0 3.816842 1.0 1.0 1589 800.0 3.816842 2.0 1.0 1590 800.0 3.816842 3.0 1.0 1591 800.0 3.816842 4.0 1.0 1592 800.0 3.908421 1.0 1.0 1593 800.0 3.908421 2.0 1.0 1594 800.0 3.908421 3.0 1.0 1595 800.0 3.908421 4.0 1.0 1596 800.0 4.000000 1.0 1.0 1597 800.0 4.000000 2.0 1.0 1598 800.0 4.000000 3.0 1.0 1599 800.0 4.000000 4.0 1.0 [1600 rows x 4 columns] ranks_1 ranks_2 ranks_3 ranks_4 0 1.0 0.0 0.0 0.0 1 0.0 1.0 0.0 0.0 2 0.0 0.0 1.0 0.0 3 0.0 0.0 0.0 1.0 4 1.0 0.0 0.0 0.0 5 0.0 1.0 0.0 0.0 6 0.0 0.0 1.0 0.0 7 0.0 0.0 0.0 1.0 8 1.0 0.0 0.0 0.0 9 0.0 1.0 0.0 0.0 10 0.0 0.0 1.0 0.0 11 0.0 0.0 0.0 1.0 12 1.0 0.0 0.0 0.0 13 0.0 1.0 0.0 0.0 14 0.0 0.0 1.0 0.0 15 0.0 0.0 0.0 1.0 16 1.0 0.0 0.0 0.0 17 0.0 1.0 0.0 0.0 18 0.0 0.0 1.0 0.0 19 0.0 0.0 0.0 1.0 20 1.0 0.0 0.0 0.0 21 0.0 1.0 0.0 0.0 22 0.0 0.0 1.0 0.0 23 0.0 0.0 0.0 1.0 24 1.0 0.0 0.0 0.0 25 0.0 1.0 0.0 0.0 26 0.0 0.0 1.0 0.0 27 0.0 0.0 0.0 1.0 28 1.0 0.0 0.0 0.0 29 0.0 1.0 0.0 0.0 ... ... ... ... ... 1570 0.0 0.0 1.0 0.0 1571 0.0 0.0 0.0 1.0 1572 1.0 0.0 0.0 0.0 1573 0.0 1.0 0.0 0.0 1574 0.0 0.0 1.0 0.0 1575 0.0 0.0 0.0 1.0 1576 1.0 0.0 0.0 0.0 1577 0.0 1.0 0.0 0.0 1578 0.0 0.0 1.0 0.0 1579 0.0 0.0 0.0 1.0 1580 1.0 0.0 0.0 0.0 1581 0.0 1.0 0.0 0.0 1582 0.0 0.0 1.0 0.0 1583 0.0 0.0 0.0 1.0 1584 1.0 0.0 0.0 0.0 1585 0.0 1.0 0.0 0.0 1586 0.0 0.0 1.0 0.0 1587 0.0 0.0 0.0 1.0 1588 1.0 0.0 0.0 0.0 1589 0.0 1.0 0.0 0.0 1590 0.0 0.0 1.0 0.0 1591 0.0 0.0 0.0 1.0 1592 1.0 0.0 0.0 0.0 1593 0.0 1.0 0.0 0.0 1594 0.0 0.0 1.0 0.0 1595 0.0 0.0 0.0 1.0 1596 1.0 0.0 0.0 0.0 1597 0.0 1.0 0.0 0.0 1598 0.0 0.0 1.0 0.0 1599 0.0 0.0 0.0 1.0 [1600 rows x 4 columns] *********6 gre gpa ranks_2 ranks_3 ranks_4 0 220.0 2.260000 0.0 0.0 0.0 1 220.0 2.260000 1.0 0.0 0.0 2 220.0 2.260000 0.0 1.0 0.0 3 220.0 2.260000 0.0 0.0 1.0 4 220.0 2.351579 0.0 0.0 0.0 5 220.0 2.351579 1.0 0.0 0.0 6 220.0 2.351579 0.0 1.0 0.0 7 220.0 2.351579 0.0 0.0 1.0 8 220.0 2.443158 0.0 0.0 0.0 9 220.0 2.443158 1.0 0.0 0.0 10 220.0 2.443158 0.0 1.0 0.0 11 220.0 2.443158 0.0 0.0 1.0 12 220.0 2.534737 0.0 0.0 0.0 13 220.0 2.534737 1.0 0.0 0.0 14 220.0 2.534737 0.0 1.0 0.0 15 220.0 2.534737 0.0 0.0 1.0 16 220.0 2.626316 0.0 0.0 0.0 17 220.0 2.626316 1.0 0.0 0.0 18 220.0 2.626316 0.0 1.0 0.0 19 220.0 2.626316 0.0 0.0 1.0 20 220.0 2.717895 0.0 0.0 0.0 21 220.0 2.717895 1.0 0.0 0.0 22 220.0 2.717895 0.0 1.0 0.0 23 220.0 2.717895 0.0 0.0 1.0 24 220.0 2.809474 0.0 0.0 0.0 25 220.0 2.809474 1.0 0.0 0.0 26 220.0 2.809474 0.0 1.0 0.0 27 220.0 2.809474 0.0 0.0 1.0 28 220.0 2.901053 0.0 0.0 0.0 29 220.0 2.901053 1.0 0.0 0.0 ... ... ... ... ... ... 1570 800.0 3.358947 0.0 1.0 0.0 1571 800.0 3.358947 0.0 0.0 1.0 1572 800.0 3.450526 0.0 0.0 0.0 1573 800.0 3.450526 1.0 0.0 0.0 1574 800.0 3.450526 0.0 1.0 0.0 1575 800.0 3.450526 0.0 0.0 1.0 1576 800.0 3.542105 0.0 0.0 0.0 1577 800.0 3.542105 1.0 0.0 0.0 1578 800.0 3.542105 0.0 1.0 0.0 1579 800.0 3.542105 0.0 0.0 1.0 1580 800.0 3.633684 0.0 0.0 0.0 1581 800.0 3.633684 1.0 0.0 0.0 1582 800.0 3.633684 0.0 1.0 0.0 1583 800.0 3.633684 0.0 0.0 1.0 1584 800.0 3.725263 0.0 0.0 0.0 1585 800.0 3.725263 1.0 0.0 0.0 1586 800.0 3.725263 0.0 1.0 0.0 1587 800.0 3.725263 0.0 0.0 1.0 1588 800.0 3.816842 0.0 0.0 0.0 1589 800.0 3.816842 1.0 0.0 0.0 1590 800.0 3.816842 0.0 1.0 0.0 1591 800.0 3.816842 0.0 0.0 1.0 1592 800.0 3.908421 0.0 0.0 0.0 1593 800.0 3.908421 1.0 0.0 0.0 1594 800.0 3.908421 0.0 1.0 0.0 1595 800.0 3.908421 0.0 0.0 1.0 1596 800.0 4.000000 0.0 0.0 0.0 1597 800.0 4.000000 1.0 0.0 0.0 1598 800.0 4.000000 0.0 1.0 0.0 1599 800.0 4.000000 0.0 0.0 1.0 [1600 rows x 5 columns] [0 0 0 ..., 0 0 0] gre gpa ranks intercept predict_admit 0 220.0 2.260000 1.0 1.0 0 1 220.0 2.260000 2.0 1.0 0 2 220.0 2.260000 3.0 1.0 0 3 220.0 2.260000 4.0 1.0 0 4 220.0 2.351579 1.0 1.0 0 5 220.0 2.351579 2.0 1.0 0 6 220.0 2.351579 3.0 1.0 0 7 220.0 2.351579 4.0 1.0 0 8 220.0 2.443158 1.0 1.0 0 9 220.0 2.443158 2.0 1.0 0 10 220.0 2.443158 3.0 1.0 0 11 220.0 2.443158 4.0 1.0 0 12 220.0 2.534737 1.0 1.0 0 13 220.0 2.534737 2.0 1.0 0 14 220.0 2.534737 3.0 1.0 0 15 220.0 2.534737 4.0 1.0 0 16 220.0 2.626316 1.0 1.0 0 17 220.0 2.626316 2.0 1.0 0 18 220.0 2.626316 3.0 1.0 0 19 220.0 2.626316 4.0 1.0 0 20 220.0 2.717895 1.0 1.0 0 21 220.0 2.717895 2.0 1.0 0 22 220.0 2.717895 3.0 1.0 0 23 220.0 2.717895 4.0 1.0 0 24 220.0 2.809474 1.0 1.0 0 25 220.0 2.809474 2.0 1.0 0 26 220.0 2.809474 3.0 1.0 0 27 220.0 2.809474 4.0 1.0 0 28 220.0 2.901053 1.0 1.0 0 29 220.0 2.901053 2.0 1.0 0 ... ... ... ... ... ... 1570 800.0 3.358947 3.0 1.0 0 1571 800.0 3.358947 4.0 1.0 0 1572 800.0 3.450526 1.0 1.0 1 1573 800.0 3.450526 2.0 1.0 0 1574 800.0 3.450526 3.0 1.0 0 1575 800.0 3.450526 4.0 1.0 0 1576 800.0 3.542105 1.0 1.0 1 1577 800.0 3.542105 2.0 1.0 0 1578 800.0 3.542105 3.0 1.0 0 1579 800.0 3.542105 4.0 1.0 0 1580 800.0 3.633684 1.0 1.0 1 1581 800.0 3.633684 2.0 1.0 0 1582 800.0 3.633684 3.0 1.0 0 1583 800.0 3.633684 4.0 1.0 0 1584 800.0 3.725263 1.0 1.0 1 1585 800.0 3.725263 2.0 1.0 0 1586 800.0 3.725263 3.0 1.0 0 1587 800.0 3.725263 4.0 1.0 0 1588 800.0 3.816842 1.0 1.0 1 1589 800.0 3.816842 2.0 1.0 0 1590 800.0 3.816842 3.0 1.0 0 1591 800.0 3.816842 4.0 1.0 0 1592 800.0 3.908421 1.0 1.0 1 1593 800.0 3.908421 2.0 1.0 0 1594 800.0 3.908421 3.0 1.0 0 1595 800.0 3.908421 4.0 1.0 0 1596 800.0 4.000000 1.0 1.0 1 1597 800.0 4.000000 2.0 1.0 0 1598 800.0 4.000000 3.0 1.0 0 1599 800.0 4.000000 4.0 1.0 0 [1600 rows x 5 columns] predict_admit gre ranks 220.000000 1.0 0.00 2.0 0.00 3.0 0.00 4.0 0.00 250.526316 1.0 0.00 2.0 0.00 3.0 0.00 4.0 0.00 281.052632 1.0 0.00 2.0 0.00 3.0 0.00 4.0 0.00 311.578947 1.0 0.00 2.0 0.00 3.0 0.00 4.0 0.00 342.105263 1.0 0.00 2.0 0.00 3.0 0.00 4.0 0.00 372.631579 1.0 0.00 2.0 0.00 3.0 0.00 4.0 0.00 403.157895 1.0 0.00 2.0 0.00 3.0 0.00 4.0 0.00 433.684211 1.0 0.00 2.0 0.00 ... ... 586.315789 3.0 0.00 4.0 0.00 616.842105 1.0 0.40 2.0 0.00 3.0 0.00 4.0 0.00 647.368421 1.0 0.50 2.0 0.00 3.0 0.00 4.0 0.00 677.894737 1.0 0.60 2.0 0.00 3.0 0.00 4.0 0.00 708.421053 1.0 0.65 2.0 0.00 3.0 0.00 4.0 0.00 738.947368 1.0 0.75 2.0 0.00 3.0 0.00 4.0 0.00 769.473684 1.0 0.85 2.0 0.00 3.0 0.00 4.0 0.00 800.000000 1.0 0.95 2.0 0.00 3.0 0.00 4.0 0.00 [80 rows x 1 columns] *********9 Float64Index([1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0], dtype=‘float64‘, name=u‘ranks‘) Float64Index([ 220.0, 250.526315789, 281.052631579, 311.578947368, 342.105263158, 372.631578947, 403.157894737, 433.684210526, 464.210526316, 494.736842105, 525.263157895, 555.789473684, 586.315789474, 616.842105263, 647.368421053, 677.894736842, 708.421052632, 738.947368421, 769.473684211, 800.0], dtype=‘float64‘, name=u‘gre‘)
邏輯回歸--數據獨熱編碼＋數據結果可視化