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機器學習與基礎演算法, Support vector machine (SVM), (預測的數值型態), (線性方法實現非線性分類) -…

- - - - Variance代表模型本身的不穩定度(與trining error相關)
    - - noise代表資料本身的錯誤
    - - 模型與真實狀況之間的誤差(與validation error相關)
  - - - Scaling
      - Standardize
      - Threshokling
      - Apply Log / Exp. tunction
    - - Under-Sample
        
        Random Under-Sample
      - Over-Sample
        
        Smote
- - - - 預測的數值型態
  - - - 分群(clutering)
        
        K Nearest Neighbor
        
        Hierarchical Clustering (階層式分群法)
- - - - Regularized Regression (正規化迴歸)
        
        penalty (懲罰項)
        
        Ridge (L2)
        
        相信大部分的feature
        都會對結果有小影響
        
        Lasso (L1)
        
        相信小部分的feature
        都會對結果有大影響
        
        可以做出特徵篩選
      - Support vector regression (SVR)
        
        Tube
        
        計算Tube(寬度=margin的距離)外數據的誤差
      - linear regression (線性迴歸)
        
        衍生產物
        
        Gradient descent (梯度下降)
        (GD)
        
        stochastic gradient descent (SGD)
        
        傳統GD
        
        Mini-batch GD
        
        計算RMSE
      - One-Hot encoding
        
        One-hot Encoding
        可以用迴歸實現分類問題
  - - - K-nearest neighbors (KNN)
        
        不需要訓練，real time search
        
        需要訓練，加速搜尋鄰居點
      - Logistic regression
        
        likelihood and log likelihood
        
        +1* log likelihood = cross entropy loss
        
        cross entropy loss
      - Support vector classifier (SVC)
        
        Kernel 選擇
        
        "Liner" = 線性分類
        
        Radial basis function
        polynomial kernel function
      - Decision tree (決策樹)
        
        Ensemble learning
        (集成學習)
        
        Boosting
        
        XGBoost
        
        Stacking
        
        LightGBM
        
        Bagging
        
        Random Forest (隨機森林)