Scikit Learn से plot_precision_recall_curve() का उपयोग करने के बाद मैं सोच रहा था कि यह फ़ंक्शन किस औसत परिशुद्धता का उपयोग कर रहा है। दस्तावेज़ों को देखते समय, मुझे बाइनरी लक्ष्य के लिए यही मिलता है:
# %%
# Compute the average precision score
# ...................................
from sklearn.metrics import average_precision_score
average_precision = average_precision_score(y_test, y_score)
print('Average precision-recall score: {0:0.2f}'.format(
average_precision))
यह मेरा डेटा है:
clf_4 = svm.SVC()
clf_4.fit(X_train, y_train)
y_clf_4 = clf_4.predict(X_test)
y1_test = np.array([1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1]
y1_clf4 = np.array([0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1]
average_precision_5 = average_precision_score(y1_test, y1_clf4)
average_precision_5
Out: 0.5625
अब हम x_test के साथ प्लॉट_प्रेसिजन_रेकॉल_कर्व का उपयोग करते हैं (ऊपर जैसा ही):
X_test= np.array([[0.01167537, 0.04676259, 0.02145552, 0.015625 , 0. ,
0. , 0. , 0.5 , 0.01020408, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 1. , 0. ,
0. , 0. , 0. , 1. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.00478415, 0.01258993, 0.06759886, 0.09375 , 0. ,
0. , 0. , 0.43421053, 0. , 1. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.01503446, 0.04136691, 0.02600806, 0.015625 , 0. ,
0. , 1. , 0.13157895, 0.02721088, 0. ,
0. , 0. , 0. , 0. , 0. ,
1. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.017396 , 0.04856115, 0.07737383, 0.046875 , 0. ,
0. , 0. , 0.44736842, 0.04421769, 0. ,
0. , 1. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
1. , 0. , 0. , 1. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.0072882 , 0.01079137, 0.07866155, 0.078125 , 1. ,
0. , 0. , 0.63157895, 0. , 1. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.00733909, 0.0323741 , 0.0487578 , 0.046875 , 0. ,
0. , 0. , 0.44736842, 0.02040816, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 1. , 0. , 0. , 1. ,
0. , 1. , 0. , 0. , 0. ,
0. , 0. , 1. , 0. , 0. ,
0. ],
[0.02579371, 0.11151079, 0.03639438, 0.0625 , 0. ,
0. , 0. , 0.53947368, 0.02380952, 0. ,
0. , 0. , 1. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 1. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.00203581, 0.03417266, 0.12611863, 0.125 , 0. ,
0. , 0. , 0.05263158, 0.00680272, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1. ,
0. , 0. , 1. , 0. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.00527275, 0.03057554, 0.0344563 , 0.03125 , 0. ,
0. , 1. , 0.09210526, 0.00680272, 1. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.00590385, 0.02158273, 0.05135926, 0.046875 , 0. ,
0. , 0. , 0.43421053, 0.00340136, 1. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1. ,
0. , 0. , 0. , 1. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.01910608, 0.16366906, 0.05917014, 0.03125 , 1. ,
0. , 1. , 0.28947368, 0.12244898, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 1. , 0. , 0. , 1. ,
0. , 0. , 1. , 0. , 0. ,
0. , 0. , 1. , 0. , 0. ,
0. ],
[0.12737045, 0.13669065, 0.07280827, 0.078125 , 1. ,
0. , 0. , 0.46052632, 0.07823129, 0. ,
0. , 1. , 0. , 0. , 0. ,
0. , 1. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
1. , 1. , 0. , 0. , 0. ,
0. , 0. , 0. , 1. , 0. ,
0. ],
[0.0537861 , 0.17446043, 0.14109651, 0.078125 , 0. ,
0. , 0. , 0.32894737, 0.08843537, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1. ,
0. , 0. , 1. , 0. , 0. ,
0. , 1. , 0. , 0. , 1. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.01027066, 0.05755396, 0.06110172, 0.078125 , 1. ,
0. , 0. , 0.30263158, 0.01360544, 1. ,
0. , 0. , 0. , 0. , 1. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 1. , 0. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.0085504 , 0.01978417, 0.03185484, 0.03125 , 1. ,
1. , 0. , 0.51315789, 0.00340136, 0. ,
0. , 0. , 1. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 1. , 0. ,
0. , 1. , 0. , 0. , 0. ,
0. , 0. , 0. , 1. , 0. ,
0. ],
[0.02224122, 0.05215827, 0.06370968, 0.0625 , 0. ,
0. , 0. , 0.47368421, 0.04081633, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1. ,
0. , 0. , 0. , 1. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.00896774, 0.05035971, 0.00974896, 0.015625 , 0. ,
0. , 0. , 0.5 , 0.02721088, 0. ,
0. , 0. , 0. , 0. , 0. ,
1. , 0. , 0. , 0. , 0. ,
0. , 0. , 1. , 0. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.03302084, 0.07014388, 0.00779787, 0.015625 , 1. ,
1. , 0. , 0.25 , 0.03741497, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 1. , 0. ,
0. , 0. , 1. , 0. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.00630083, 0.06115108, 0.01495838, 0. , 0. ,
0. , 0. , 0.10526316, 0.00340136, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 1. , 0. ,
0. , 0. , 1. , 0. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[0.00951741, 0.03776978, 0.13261576, 0.140625 , 1. ,
1. , 0. , 0.47368421, 0.0170068 , 1. ,
0. , 1. , 0. , 0. , 0. ,
0. , 1. , 0. , 0. , 0. ,
0. , 0. , 1. , 0. , 0. ,
0. , 1. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ]])
अब हम plot_precision_recall_curve फ़ंक्शन का उपयोग कर सकते हैं और दो परिणामों को प्रिंट कर सकते हैं, और वे भिन्न हैं:
disp = plot_precision_recall_curve(clf_4, X_test, y1_test)
disp.ax_.set_title(f'2-class Precision-Recall curve:{average_precision_5}')
तो अंतर कहाँ से आता है?
0
30 सितंबर 2020, 16:42
1 उत्तर
सबसे बढ़िया उत्तर
y_score के पैरामीटर average_precision_score को संभाव्यता अनुमान (या एक समान निरंतर स्कोर) होना चाहिए, न कि कठिन वर्गीकरण परिणाम। तो आपका average_precision_5 गलत है।
1
30 सितंबर 2020, 20:22