Machine Learning Experiment4 Logistic Regression and Newton’s Method 詳解+源代碼
回顧Logistic Regression的基本原理 關於sigmoid函數 極大似然與損失函數 牛頓法 實驗步驟與過程 首先，讀入數據並繪製原始數據散點圖 根據圖像，咱們能夠看出，左下大多爲負樣本，而右上多爲正樣本，劃分應該大體爲一個斜率爲負的直線。python 定義預測方程： 此處使用sigmoid函數，定義爲匿名函數（由於在MATLAB中內聯函數即將被淘汰）函數 定義損失函數和迭代次數
相關文章
- 1. Machine Learning Experiment4： Logistic Regression and Newton’s Method 詳解+源代碼解析
- 2. Machine Learning—Classification and logistic regression
- 3. Machine Learning - Logistic Regression - Two-class Classification
- 4. Machine Learning in Action (5) —— logistic regression
- 5. Machine Learning Series No.2 --Logistic Regression
- 6. Machine Learning Andrew Ng -6. Logistic Regression
- 7. Exercise: Logistic Regression and Newton's Method
- 8. Machine Learning experiment1 Linear Regression 詳解+源代碼實現
- 9. Machine Learning experiment2 Multivariate Linear Regression 詳解+源代碼實現
- 10. 【Machine Learning, Coursera】機器學習Week3 Logistic Regression
- 更多相關文章...
- • Markdown 代碼 - Markdown 教程
- • Eclipse 代碼模板 - Eclipse 教程
- • IntelliJ IDEA代碼格式化設置
- • Flink 數據傳輸及反壓詳解
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Machine Learning Experiment4： Logistic Regression and Newton’s Method 詳解+源代碼解析
- 2. Machine Learning—Classification and logistic regression
- 3. Machine Learning - Logistic Regression - Two-class Classification
- 4. Machine Learning in Action (5) —— logistic regression
- 5. Machine Learning Series No.2 --Logistic Regression
- 6. Machine Learning Andrew Ng -6. Logistic Regression
- 7. Exercise: Logistic Regression and Newton's Method
- 8. Machine Learning experiment1 Linear Regression 詳解+源代碼實現
- 9. Machine Learning experiment2 Multivariate Linear Regression 詳解+源代碼實現
- 10. 【Machine Learning, Coursera】機器學習Week3 Logistic Regression