---
# System prepended metadata

title: Maching Learning
tags: [Data Science]

---

###### tags: `Data Science`
# Maching Learning
## 特徵工程
> 一個1.73公尺高的人，體重90公斤，特徵會比較偏向體重，所以需要做特徵縮放(Feature Scaling)
- **Min-Max Scaling** => 將結果映射到[0,1]之間。
![](https://miro.medium.com/max/3604/1*QjZVCDwW1TMFTWF3rubemQ@2x.png)
![](https://miro.medium.com/max/762/1*-9SPkqHA12dkiDCAHnXLuw.png)

- **標準化值正規化(Z-Score Normalization or Standardlization)**  => 映射到平均值為0，標準差為1的分布。
![](https://miro.medium.com/max/744/1*GAcEj37smCOCZMrqp-rjjA.png)
$$
z = {x - \mu \over \sigma}
$$

## 經典演算法
**SVM** [[Link]](https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC3-4%E8%AC%9B-%E6%94%AF%E6%8F%B4%E5%90%91%E9%87%8F%E6%A9%9F-support-vector-machine-%E4%BB%8B%E7%B4%B9-9c6c6925856b)

**Random Forest** [[Link]](https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC3-5%E8%AC%9B-%E6%B1%BA%E7%AD%96%E6%A8%B9-decision-tree-%E4%BB%A5%E5%8F%8A%E9%9A%A8%E6%A9%9F%E6%A3%AE%E6%9E%97-random-forest-%E4%BB%8B%E7%B4%B9-7079b0ddfbda)

**XGBoost** [[Link]](https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC5-2%E8%AC%9B-kaggle%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92%E7%AB%B6%E8%B3%BD%E7%A5%9E%E5%99%A8xgboost%E4%BB%8B%E7%B4%B9-1c8f55cffcc)

## 降維
主成分分析 Principal Componant Analysis
[世上最生動的PCA：直觀理解並應用主成分分析 - LeeMeng](https://leemeng.tw/essence-of-principal-component-analysis.html#top)
