調べたことやコード変更した内容について下記のようにコメントを入れることにしています。

  #【コメント】コメントです

#https://ohke.hateblo.jp/entry/2017/08/11/230000を参考に利用しています。

Googleドライブのマウント¶

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import confusion_matrix
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
%matplotlib inline

sys.pathの設定¶

以下では，Googleドライブのマイドライブ直下にstudy_ai_mlフォルダを置くことを仮定しています．必要に応じて，パスを変更してください。

cancer_df = pd.read_csv('/content/drive/My Drive/study_ai_ml_google/data/cancer.csv')

print('cancer df shape: {}'.format(cancer_df.shape))

cancer df shape: (569, 33)

cancer_df

cancer_df.drop('Unnamed: 32', axis=1, inplace=True)
cancer_df

・diagnosis: 診断結果 (良性がB / 悪性がM) ・説明変数は3列以降、目的変数を2列目としロジスティック回帰で分類

# 目的変数の抽出
y = cancer_df.diagnosis.apply(lambda d: 1 if d == 'M' else 0)
y

0      1
1      1
2      1
3      1
4      1
      ..
564    1
565    1
566    1
567    1
568    0
Name: diagnosis, Length: 569, dtype: int64

# 説明変数の抽出
X = cancer_df.loc[:, 'radius_mean':]
X

# 学習用とテスト用でデータを分離
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# 標準化
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# ロジスティック回帰で学習
logistic = LogisticRegressionCV(cv=10, random_state=0,max_iter=1000)
  #【コメント】イテレーションを増やすようにというWarningが出ていたので100にした
logistic.fit(X_train_scaled, y_train)

# 検証
print('Train score: {:.3f}'.format(logistic.score(X_train_scaled, y_train)))
print('Test score: {:.3f}'.format(logistic.score(X_test_scaled, y_test)))
print('Confustion matrix:\n{}'.format(confusion_matrix(y_true=y_test, y_pred=logistic.predict(X_test_scaled))))

Train score: 0.988
Test score: 0.972
Confustion matrix:
[[89  1]
 [ 3 50]]

・検証スコア97%で分類できることを確認

pca = PCA(n_components=30)
pca.fit(X_train_scaled)
plt.bar([n for n in range(1, len(pca.explained_variance_ratio_)+1)], pca.explained_variance_ratio_)
  #【コメント】各次元の寄与率の可視化
  #【コメント】一般的に累積寄与率が80%となるよう、主成分を抽出すると良いらしい(このデータだと4から5次元くらいまで？)

<BarContainer object of 30 artists>

# PCA
# 次元数2まで圧縮
pca = PCA(n_components=2)
X_train_pca = pca.fit_transform(X_train_scaled)
print('X_train_pca shape: {}'.format(X_train_pca.shape))
# X_train_pca shape: (426, 2)
X_train_pca
  #【コメント】 2次元になっている↓

X_train_pca shape: (426, 2)

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       [ 3.31088863e+00, -1.65939327e+00],
       [-3.12337049e+00, -1.92657856e+00],
       [ 5.00812584e+00, -1.17383608e+00],
       [-6.60728916e-01,  1.78510222e+00],
       [ 6.25554401e+00,  1.14757178e+00],
       [-3.32980445e-01, -9.88442588e-01],
       [-2.46580519e+00, -2.02959456e+00],
       [ 2.31208360e+00,  4.58431137e+00],
       [-3.88156898e+00,  1.08240797e+00],
       [-1.29131997e+00,  9.34130409e-01],
       [-3.32442070e+00, -8.47415896e-01],
       [ 5.18819885e+00, -2.21553456e+00],
       [ 7.87674197e-01, -1.56145504e+00],
       [ 4.58203743e+00,  4.83978852e-01],
       [-1.64242158e+00,  1.73988692e+00],
       [-5.42471122e+00,  4.77409505e-01],
       [ 8.74079598e+00,  3.63711992e+00],
       [ 2.69324550e+00,  1.73128614e-01],
       [-4.60707340e+00, -2.83900047e+00],
       [-4.79526420e-01, -9.22611535e-01],
       [ 5.89385302e-01,  2.77395633e-01],
       [-1.47351784e+00,  1.71611168e+00],
       [ 1.33730462e+00,  9.81948610e-01],
       [ 1.82231573e+00,  2.88998076e+00],
       [-3.74738325e+00, -1.84165435e+00],
       [-3.08730294e-02,  2.29738556e+00],
       [-4.50451659e+00, -3.24612629e+00],
       [-2.91179143e+00,  3.27834406e-01],
       [-3.56085734e+00, -1.32616622e+00],
       [-1.99695736e+00, -2.47535686e+00],
       [-2.74199332e+00, -2.16327060e+00],
       [-1.64919372e+00,  2.48401519e+00],
       [-4.42608805e+00, -8.69927284e-01],
       [ 8.64544242e+00, -3.19786856e+00],
       [ 2.54141935e+00, -2.35730784e+00],
       [-7.40113097e-01, -5.18784054e-02],
       [-2.97598746e+00,  1.77375924e+00],
       [-3.36427340e+00, -7.54150773e-02],
       [ 4.83863925e+00,  3.23691356e+00],
       [-5.51825211e+00, -7.73630580e-01],
       [-3.05793601e+00, -2.22100606e+00],
       [-1.85775451e+00,  1.39851943e+00],
       [-2.80981317e+00, -4.05238960e-01],
       [ 4.21342556e+00, -4.98812885e+00],
       [-2.95696557e+00, -7.09572756e-01],
       [ 4.16646616e+00,  3.01282388e+00],
       [ 1.03933388e+00,  1.02169754e+00],
       [ 4.22800713e+00,  1.32538252e+00],
       [-2.00444883e+00,  4.21084778e-01],
       [ 9.21475147e+00,  2.19772974e+00],
       [-3.52645638e-01,  2.20207995e+00],
       [ 1.70657037e+00, -2.00123218e+00],
       [-2.33651837e+00,  7.14403093e-01],
       [ 5.24198043e+00, -6.53178325e+00],
       [ 1.61490244e+01, -7.24611405e+00],
       [-2.42421736e+00,  5.76192085e-01],
       [-2.77905225e+00,  3.40496767e-01],
       [-3.42249621e+00, -2.48502091e+00],
       [-1.93414823e+00,  1.42872198e+00],
       [-3.03794269e+00, -1.89637954e-01],
       [-2.89394159e+00, -3.48525482e-02],
       [-1.39527531e+00,  1.42061067e+00],
       [-4.98922907e+00, -3.48023351e+00],
       [ 7.28191791e+00, -3.44747764e+00],
       [ 6.42282927e+00, -1.61219289e+00],
       [ 2.85230648e+00,  7.23177385e-01],
       [ 1.24756825e+00, -2.31255427e+00],
       [ 4.37071230e+00, -1.96779348e+00],
       [-5.49703234e-02, -1.19736251e-01],
       [-1.29663111e+00,  1.54010964e+00],
       [-1.95475015e+00, -1.85472567e+00],
       [-3.24845224e+00, -9.64474413e-01],
       [-3.12152066e+00, -3.36486268e-01],
       [-2.08953432e+00,  6.28456436e-02],
       [-1.90796477e+00,  2.56172011e+00],
       [ 3.56564947e+00, -3.71340906e+00],
       [-1.89951941e+00, -6.06163363e-01],
       [ 4.20915999e-01, -2.74923340e+00],
       [-1.84859038e+00, -9.18591681e-01],
       [ 3.42045099e-01,  1.69806524e+00],
       [-3.36897374e-01, -5.75581704e-01],
       [-3.54310230e-01, -3.32406750e-01],
       [-1.81253092e+00, -1.10977930e-01],
       [ 9.54219195e+00, -5.32746910e+00],
       [ 1.48118077e+00, -9.85472568e-01],
       [ 1.76641962e+00,  2.41951459e+00],
       [-3.12026972e+00, -7.89515570e-01],
       [ 4.59018860e+00,  3.21424161e+00],
       [-2.16853356e+00,  4.68564919e-01],
       [-3.75623266e+00, -3.81717458e-01],
       [ 2.81679892e-01, -4.13729593e-01],
       [ 1.08542269e+01, -1.89004497e+00],
       [ 5.08560754e+00,  3.23758776e+00],
       [ 7.56203604e-01, -2.29298747e+00],
       [-3.78502513e-01, -7.76399742e-01],
       [ 2.21777653e+00,  1.04172770e+00],
       [ 1.81985826e+00, -4.23182624e+00],
       [ 5.33380606e+00,  4.10137976e+00],
       [-3.42475266e+00, -2.17190888e+00],
       [ 9.04537882e+00,  2.33752916e+00],
       [-4.45929164e-01,  3.86055779e+00],
       [ 3.30121037e+00, -1.42796866e+00],
       [-3.63721835e+00,  1.59165031e+00],
       [-1.71011727e+00,  1.00972610e+00],
       [ 3.27958813e+00, -1.04055950e+00],
       [ 5.74162486e-01,  4.54631649e-01],
       [ 1.46523027e+00,  1.44386747e+00],
       [ 4.07811727e+00,  6.45074106e-01],
       [-2.64190057e+00,  6.02524612e-01],
       [-4.08982337e+00, -5.29707118e-01],
       [-1.84357047e+00,  1.73549435e+00],
       [ 1.03893032e-01,  7.26493782e+00],
       [ 3.81682257e+00, -8.43166568e-01],
       [-2.37124119e+00, -1.69173266e+00],
       [-1.63870782e+00, -1.97805600e-01],
       [ 6.54993668e+00, -5.80871669e+00],
       [ 3.71474288e-01,  3.97520197e+00],
       [-3.21380062e-01,  1.58980046e-01],
       [ 3.61544513e-01,  1.18595929e+00],
       [ 3.03183473e+00,  8.69697874e-01],
       [ 6.51808828e-01,  7.38291727e-01],
       [-1.21826465e+00, -1.56206981e+00],
       [-8.89951063e-01,  2.54182711e+00],
       [-2.00662011e+00,  3.05064104e-01],
       [ 5.58670329e+00,  1.11452916e+00],
       [ 3.93457631e-01,  3.64475677e+00],
       [-4.71055375e+00, -3.06342001e-01],
       [ 3.60862610e+00, -2.09427033e+00],
       [-2.05349968e+00,  1.24324055e+00],
       [-2.24850174e+00, -2.48904595e+00],
       [-3.86328935e+00,  4.39682987e+00],
       [-4.48217238e-01,  1.52081858e-01],
       [-1.34839911e+00, -1.76992390e+00],
       [ 2.71067086e+00, -4.32043003e+00],
       [ 3.47604839e-02, -3.37853851e+00],
       [ 6.49029629e+00,  7.94277378e+00],
       [-3.20758299e+00,  5.43091464e-01],
       [-5.69315584e+00, -5.73866830e-01],
       [-1.07704361e+00,  1.37951070e+00]])

# 寄与率
print('explained variance ratio: {}'.format(pca.explained_variance_ratio_))
# explained variance ratio: [ 0.43315126  0.19586506]
  #【コメント】累積で60%ちょいくらい

explained variance ratio: [0.43315126 0.19586506]

# 散布図にプロット
temp = pd.DataFrame(X_train_pca)
# 散布図にプロット
temp = pd.DataFrame(X_train_pca)
temp['Outcome'] = y_train.values
temp

b = temp[temp['Outcome'] == 0]
m = temp[temp['Outcome'] == 1]
plt.scatter(x=b[0], y=b[1], marker='o') # 良性は○でマーク
plt.scatter(x=m[0], y=m[1], marker='^') # 悪性は△でマーク
plt.xlabel('PC 1') # 第1主成分をx軸
plt.ylabel('PC 2') # 第2主成分をy軸
  #【コメント】2次元に次元圧縮してもそれなりに分類できそう

X_train_pca shape: (426, 2)
explained variance ratio: [0.43315126 0.19586506]

Text(0, 0.5, 'PC 2')

  #【コメント】次元数2まで圧縮してロジスティック回帰してみる
# 次元数2まで圧縮
pca = PCA(n_components=2)
X_train_pca = pca.fit_transform(X_train_scaled)
X_test_pca = pca.fit_transform(X_test_scaled)


# ロジスティック回帰で学習
logistic = LogisticRegressionCV(cv=10, random_state=0,max_iter=1000)
  #【コメント】イテレーションを増やすようにというWarningが出ていたので100にした
logistic.fit(X_train_pca, y_train)

# 検証
print('Train score: {:.3f}'.format(logistic.score(X_train_pca, y_train)))
print('Test score: {:.3f}'.format(logistic.score(X_test_pca, y_test)))
print('Confustion matrix:\n{}'.format(confusion_matrix(y_true=y_test, y_pred=logistic.predict(X_test_pca))))
  #【コメント】検証スコア91.6%

Train score: 0.965
Test score: 0.916
Confustion matrix:
[[83  7]
 [ 5 48]]

import numpy as np

#【コメント】1-15次元までのスコアと処理時間を可視化してみる
import time

n_max = 15
n_array = np.arange(1,n_max+1,1)
scores = np.zeros(n_max)
times = np.zeros(n_max)

for n in n_array:
  #開始時間記憶
  start_time = time.perf_counter()

  # 次元数nまで圧縮
  pca = PCA(n_components=n)
  X_train_pca = pca.fit_transform(X_train_scaled)
  X_test_pca = pca.fit_transform(X_test_scaled)
  # ロジスティック回帰で学習
  logistic = LogisticRegressionCV(cv=10, random_state=0,max_iter=1000)
    #【コメント】イテレーションを増やすようにというWarningが出ていたので100にした
  logistic.fit(X_train_pca, y_train)
  #終了時間記憶
  end_time = time.perf_counter()

  scores[n-1] = logistic.score(X_test_pca, y_test)
  times[n-1] = end_time - start_time

fig = plt.figure()
ax1 = fig.add_subplot(111)
ln1=ax1.plot(n_array, scores,'C0',label=r'$score$')

ax2 = ax1.twinx()
ln2=ax2.plot(n_array,times,'C1',label=r'$time$')

h1, l1 = ax1.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
ax1.legend(h1+h2, l1+l2, loc='lower right')

ax1.set_xlabel('n')
ax1.set_ylabel(r'$score$')
ax1.grid(True)
ax2.set_ylabel(r'$time[s]$')

#plt.plot(n_array,scores)
#plt.plot(n_array,times)
#【コメント】scoreは単純に比例していくわけではない←想像してたのと違うなんで？
#【コメント】timeは実行ごとに違うけどおおむね比例傾向。サーバーの他の処理状況によって変動はありそうなので納得できる

Text(0, 0.5, '$time[s]$')

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	radius_se	texture_se	perimeter_se	area_se	smoothness_se	compactness_se	concavity_se	concave points_se	symmetry_se	fractal_dimension_se	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	Unnamed: 32
0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.30010	0.14710	0.2419	0.07871	1.0950	0.9053	8.589	153.40	0.006399	0.04904	0.05373	0.01587	0.03003	0.006193	25.380	17.33	184.60	2019.0	0.16220	0.66560	0.7119	0.2654	0.4601	0.11890	NaN
1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.08690	0.07017	0.1812	0.05667	0.5435	0.7339	3.398	74.08	0.005225	0.01308	0.01860	0.01340	0.01389	0.003532	24.990	23.41	158.80	1956.0	0.12380	0.18660	0.2416	0.1860	0.2750	0.08902	NaN
2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.19740	0.12790	0.2069	0.05999	0.7456	0.7869	4.585	94.03	0.006150	0.04006	0.03832	0.02058	0.02250	0.004571	23.570	25.53	152.50	1709.0	0.14440	0.42450	0.4504	0.2430	0.3613	0.08758	NaN
3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.24140	0.10520	0.2597	0.09744	0.4956	1.1560	3.445	27.23	0.009110	0.07458	0.05661	0.01867	0.05963	0.009208	14.910	26.50	98.87	567.7	0.20980	0.86630	0.6869	0.2575	0.6638	0.17300	NaN
4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.19800	0.10430	0.1809	0.05883	0.7572	0.7813	5.438	94.44	0.011490	0.02461	0.05688	0.01885	0.01756	0.005115	22.540	16.67	152.20	1575.0	0.13740	0.20500	0.4000	0.1625	0.2364	0.07678	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
564	926424	M	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	0.1726	0.05623	1.1760	1.2560	7.673	158.70	0.010300	0.02891	0.05198	0.02454	0.01114	0.004239	25.450	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115	NaN
565	926682	M	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	0.1752	0.05533	0.7655	2.4630	5.203	99.04	0.005769	0.02423	0.03950	0.01678	0.01898	0.002498	23.690	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637	NaN
566	926954	M	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	0.1590	0.05648	0.4564	1.0750	3.425	48.55	0.005903	0.03731	0.04730	0.01557	0.01318	0.003892	18.980	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820	NaN
567	927241	M	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	0.2397	0.07016	0.7260	1.5950	5.772	86.22	0.006522	0.06158	0.07117	0.01664	0.02324	0.006185	25.740	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400	NaN
568	92751	B	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	0.1587	0.05884	0.3857	1.4280	2.548	19.15	0.007189	0.00466	0.00000	0.00000	0.02676	0.002783	9.456	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039	NaN

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	radius_se	texture_se	perimeter_se	area_se	smoothness_se	compactness_se	concavity_se	concave points_se	symmetry_se	fractal_dimension_se	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.30010	0.14710	0.2419	0.07871	1.0950	0.9053	8.589	153.40	0.006399	0.04904	0.05373	0.01587	0.03003	0.006193	25.380	17.33	184.60	2019.0	0.16220	0.66560	0.7119	0.2654	0.4601	0.11890
1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.08690	0.07017	0.1812	0.05667	0.5435	0.7339	3.398	74.08	0.005225	0.01308	0.01860	0.01340	0.01389	0.003532	24.990	23.41	158.80	1956.0	0.12380	0.18660	0.2416	0.1860	0.2750	0.08902
2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.19740	0.12790	0.2069	0.05999	0.7456	0.7869	4.585	94.03	0.006150	0.04006	0.03832	0.02058	0.02250	0.004571	23.570	25.53	152.50	1709.0	0.14440	0.42450	0.4504	0.2430	0.3613	0.08758
3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.24140	0.10520	0.2597	0.09744	0.4956	1.1560	3.445	27.23	0.009110	0.07458	0.05661	0.01867	0.05963	0.009208	14.910	26.50	98.87	567.7	0.20980	0.86630	0.6869	0.2575	0.6638	0.17300
4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.19800	0.10430	0.1809	0.05883	0.7572	0.7813	5.438	94.44	0.011490	0.02461	0.05688	0.01885	0.01756	0.005115	22.540	16.67	152.20	1575.0	0.13740	0.20500	0.4000	0.1625	0.2364	0.07678
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
564	926424	M	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	0.1726	0.05623	1.1760	1.2560	7.673	158.70	0.010300	0.02891	0.05198	0.02454	0.01114	0.004239	25.450	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115
565	926682	M	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	0.1752	0.05533	0.7655	2.4630	5.203	99.04	0.005769	0.02423	0.03950	0.01678	0.01898	0.002498	23.690	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637
566	926954	M	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	0.1590	0.05648	0.4564	1.0750	3.425	48.55	0.005903	0.03731	0.04730	0.01557	0.01318	0.003892	18.980	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820
567	927241	M	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	0.2397	0.07016	0.7260	1.5950	5.772	86.22	0.006522	0.06158	0.07117	0.01664	0.02324	0.006185	25.740	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400
568	92751	B	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	0.1587	0.05884	0.3857	1.4280	2.548	19.15	0.007189	0.00466	0.00000	0.00000	0.02676	0.002783	9.456	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	radius_se	texture_se	perimeter_se	area_se	smoothness_se	compactness_se	concavity_se	concave points_se	symmetry_se	fractal_dimension_se	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.30010	0.14710	0.2419	0.07871	1.0950	0.9053	8.589	153.40	0.006399	0.04904	0.05373	0.01587	0.03003	0.006193	25.380	17.33	184.60	2019.0	0.16220	0.66560	0.7119	0.2654	0.4601	0.11890
1	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.08690	0.07017	0.1812	0.05667	0.5435	0.7339	3.398	74.08	0.005225	0.01308	0.01860	0.01340	0.01389	0.003532	24.990	23.41	158.80	1956.0	0.12380	0.18660	0.2416	0.1860	0.2750	0.08902
2	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.19740	0.12790	0.2069	0.05999	0.7456	0.7869	4.585	94.03	0.006150	0.04006	0.03832	0.02058	0.02250	0.004571	23.570	25.53	152.50	1709.0	0.14440	0.42450	0.4504	0.2430	0.3613	0.08758
3	11.42	20.38	77.58	386.1	0.14250	0.28390	0.24140	0.10520	0.2597	0.09744	0.4956	1.1560	3.445	27.23	0.009110	0.07458	0.05661	0.01867	0.05963	0.009208	14.910	26.50	98.87	567.7	0.20980	0.86630	0.6869	0.2575	0.6638	0.17300
4	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.19800	0.10430	0.1809	0.05883	0.7572	0.7813	5.438	94.44	0.011490	0.02461	0.05688	0.01885	0.01756	0.005115	22.540	16.67	152.20	1575.0	0.13740	0.20500	0.4000	0.1625	0.2364	0.07678
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
564	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	0.1726	0.05623	1.1760	1.2560	7.673	158.70	0.010300	0.02891	0.05198	0.02454	0.01114	0.004239	25.450	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115
565	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	0.1752	0.05533	0.7655	2.4630	5.203	99.04	0.005769	0.02423	0.03950	0.01678	0.01898	0.002498	23.690	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637
566	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	0.1590	0.05648	0.4564	1.0750	3.425	48.55	0.005903	0.03731	0.04730	0.01557	0.01318	0.003892	18.980	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820
567	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	0.2397	0.07016	0.7260	1.5950	5.772	86.22	0.006522	0.06158	0.07117	0.01664	0.02324	0.006185	25.740	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400
568	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	0.1587	0.05884	0.3857	1.4280	2.548	19.15	0.007189	0.00466	0.00000	0.00000	0.02676	0.002783	9.456	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039

	0	1	Outcome
0	-2.859248	-0.286145	0
1	-3.263084	1.072520	0
2	3.755513	-3.409689	1
3	-3.492632	-2.687847	0
4	-0.743517	-2.482149	1
...	...	...	...
421	0.034760	-3.378539	1
422	6.490296	7.942774	1
423	-3.207583	0.543091	0
424	-5.693156	-0.573867	0
425	-1.077044	1.379511	0