- Python 程式設計經驗
- 機器學習演算法知識
觀眾
- 開發人員
Skope-rules 是一個構建在 scikit-learn 之上的 Python 機器學習模組。
在這個講師指導的實時培訓（現場或遠端）中，參與者將學習如何使用 Python skope-rules 根據現有數據集自動生成規則。
在本次培訓結束時，參與者將能夠：
- 使用 skope-rules 從可用資料中提取規則。
- 應用 skope-rules 來執行分類，這在監督式異常檢測或不平衡分類中特別有用。
- 生成用於對新傳入數據進行分類的規則。
- 調整規則以解決欺詐檢測、預測性維護、入侵檢測、保險申請審批等方面的實際問題。
課程形式
- 在現場實驗室環境中進行部分講座、部分討論、練習和大量動手實踐。
注意
- 要申請本課程的定製培訓，請聯繫我們進行安排。
- 要瞭解有關 skope-rules 的更多資訊，請訪問：https://github.com/scikit-learn-contrib/skope-rules
介紹
- 為什麼要從數據中提取規則？
Sklearn 模組概述（決策樹/隨機 Forrest）
安裝和配置 skope-rules
案例研究：檢測信用違約率
匯入數據
使用 SkopeRules 進行不平衡分類
訓練 SkopeRules 分類器
提取規則
融合規則
將分類樹和回歸樹擬合到子樣本
選擇更高精度的規則
測試更高精度的規則
總結和結論
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