- 了解機器學習概念
- Python 程式設計經驗
觀眾
- 數據科學家
- 軟體工程師
Random Forest 是一種機器學習演算法，主要用於分類和回歸。它利用多個決策樹來生成更精確和準確的預測。
這種講師指導的現場培訓（在線或現場）面向希望使用 Random Forest 為大型數據集構建機器學習演算法的數據科學家和軟體工程師。
在本次培訓結束時，參與者將能夠：
- 設置必要的開發環境，開始使用 Random forest 構建機器學習模型。
- 瞭解 Random Forest 的優點以及如何實施它來解決分類和回歸問題。
- 在 Random Forest 中瞭解如何處理大型數據集和解釋多個決策樹。
- 通過優化超參數來評估和優化機器學習模型的性能。
課程形式
- 互動講座和討論。
- 大量的練習和練習。
- 在即時實驗室環境中動手實施。
課程自定義選項
- 要申請本課程的定製培訓，請聯繫我們進行安排。
介紹
- Random Forest 特性和優勢概述
- 了解決策樹和集成方法
開始
- 設置庫（Numpy、Pandas、Matplotlib 等）
- Random Forests 中的分類和回歸
- 使用案例和示例
實施 Random Forest
- 準備用於訓練的數據集
- 訓練機器學習模型
- 評估和提高準確性
調整 Random Forest 中的超參數
- 執行交叉驗證
- 隨機搜索和網格搜索
- 可視化訓練模型性能
- 優化超參數
最佳實踐和故障排除提示
總結和後續步驟
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