scikit learn - Coefficients from multinomial logistic regression in sklearn -

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मैं एक बहु-स्तरीय समस्या पर sklearn.linear_model.LogisticRegression चला रहा हूँ। मैं जो समझता हूं, प्रत्येक वर्ग के प्रत्येक सुविधा के लिए coef _ विशेषता का गुणांक गुणांक है मुझे जो समझ में नहीं आता है वह स्केलैर्न में व्याख्या है। उदाहरण के लिए, एसपीएसएस में आपको एक वर्ग को आधार के रूप में होगा और उसके बाद उस वर्ग के संबंध में बाधाओं का व्याख्या करना होगा, इसलिए आप वास्तव में एन -1 क्लास के गुणांक प्राप्त करेंगे। यह स्केलैन में ऐसा मामला नहीं है, जहां मैं प्रत्येक कक्षा के लिए गुणांक प्राप्त करता हूं।

एक विशेषता (चार वर्गों के लिए) के लिए एक्सपोनेंटिएटेड गुणांक उदाहरण हैं:

1.164 9 | 1.0660 | 0.9589 | 0.8607

क्या यह व्याख्या सही है: इस विशेषता के लिए एक यूनिट मूल्य में वृद्धि के साथ उस उदाहरण की संभावना को प्रथम श्रेणी में बढ़कर 16% बढ़ जाती है, फिर द्वितीय श्रेणी में ~ 7% तक और तीसरे के लिए घट जाती है और चौथी कक्षाएं?

इसके अलावा, मैं गुणांक के लिए पी-वेल की गणना कैसे कर सकता हूं?


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