April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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136
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第四章：訓練模型
在使用
Lasso
時
，
為了避免梯度下降最後在最佳值附近彈跳
，
你必須在訓
練期間逐漸減少學習速度
（
它仍然會在最佳值附近彈跳
，
但幅度會越來越
小
，
所以它會收斂
）。
Lasso
代價函式在
θ
i
= 0（i = 1, 2, …, n）
是不可微的
，
但是當任何一個
θ
i
= 0
時
，
如果改
用
次梯度向量
g
13
，
梯度下降仍然有不錯的表現
。
公式
4-11
是你可以在使用梯度下降與
Lasso
代價函數時使用的次梯度向量公式
。
公式
4-11 Lasso
回歸次梯度向量
這個
Scikit-Learn
範例使用
Lasso
類別
：
>>> from sklearn.linear_model import
Lasso
>>>
lasso_reg = Lasso(alpha=0.1)
>>>
lasso_reg.fit(X, y)
>>>
lasso_reg.predict([[1.5]])
array([1.53788174])
你也可以改用
SGDRegressor(penalty="l1")
。
彈性網路
彈性網路
（Elastic Net）
介於山嶺回歸與
Lasso
回歸之間
，
它的正則化項單純只是山嶺與
Lasso
的正則化項的混合
，
你也可以控制混合比率
r。
當
r = 0
時
，
彈性網路相當於山嶺回
歸
，
當
r = 1
時
，
它相當於
Lasso
回歸
（
見公式
4-12）。
公式
4-12
彈性網路代價函數
13
在不可微點上面的次梯度向量
，
可視為圍繞著該點的梯度向量之間的中間向量
。
含正則化的線性模型
|
137
所以什麼時候該使用一般的線性回歸
（
也就是沒有任何正則化 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.