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# [memo]ML.線形回帰(式)

## 単回帰

• 線形モデル
\hat{y}=w_1x + w_0
• 残差（residual）
\hat{y_i}-y_i
• コスト関数(二乗誤差)
J(w_0, w_1)=\frac{\sum_{i=1}^{m}{(\hat{y_i}-y_i})^2}{{2m}}\\
・斜面の傾きを計算する偏微分
• 最急降下法
• 最適化アルゴリズム
• コストを最も低くする$w_0$,$w_1$を導き出す
• 最急降下法(Gradient Decent)が一般的
• $w_0$,$w_1$の最適化
• $J$の最小化
w_0=:w_0 - \alpha \frac{\partial}{\partial w_0}J(w_0, w_1)=w_0 - \alpha\frac{1}{m}\sum_{i=0}^{m}(\hat{y} - y_i)\\
w_1=:w_1 - \alpha \frac{\partial}{\partial w_1}J(w_0, w_1)=w_1 - \alpha\frac{1}{m}\sum_{i=0}^{m}(\hat{y} - y_i)x_i\\
=:は値を更新する\\
\alphaは学習率(learning\hspace{5pt}rate)\\

## 重回帰

• 線形モデル
Y = XW
• コスト関数
J(W) = \frac{1}{2m}\sum_{i=0}^n(\hat{y}-y_i)^2 \\
= \frac{1}{2m}(XW-y)^T(XW-y)\\
• 最急降下法
W=:W - \alpha\frac{1}{m}X^T(XW-y)\\
• 正規化（feature scaling/normalization）

1) zero score normalization

x_1 = \frac{x_1-\bar{x}}{\sigma}

2) min-max normalization

x_1 = \frac{x_1-x_{min}}{x_{max}-x_{min}}

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