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課程簡介
介紹
- Random Forest 特點和優勢概述
- 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
- 用例和示例
實現 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
摘要和後續步驟
最低要求
- 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
14 時間:
客戶評論 (4)
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
保持簡短和簡單。 圍繞概念創建直覺和視覺模型(決策樹圖、線性方程、手動計算y_pred以證明模型的工作原理)。
Nicolae - DB Global Technology
Course - Machine Learning
機器翻譯