October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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第
4
章
y_val_predict = sgd_reg.predict(X_val_poly_scaled)
val_error = mean_squared_error(y_val, y_val_predict)
if
val_error < minimum_val_error:
minimum_val_error = val_error
best_epoch = epoch
best_model = clone(sgd_reg)
请注意，在使用 warm_start = True 的情况下，当调用 fit() 方法时，它将在停止
的地方继续训练，而不是从头开始。
4.6 逻辑回归
正如第 1 章中提到过的，一些回归算法也可用于分类（反之亦然）。逻辑回归（Logistic
回归，也称为 Logit 回归）被广泛用于估算一个实例属于某个特定类别的概率。（比
如，这封电子邮件属于垃圾邮件的概率是多少？）如果预估概率超过 50%，则模型预测
该实例属于该类别（称为正类，标记为“1”），反之，则预测不是（称为负类，标记为
“0”）。这样它就成了一个二元分类器。
4.6.1 估计概率
所以逻辑回归是怎么工作的呢？与线性回归模型一样，逻辑回归模型也是计算输入特征
的加权和（加上偏置项），但是不同于线性回归模型直接输出结果，它输出的是结果的数
理逻辑值（参见公式 4-13）。
公式 4-13：逻辑回归模型的估计概率（向量化形式）
ph
ˆ
= =
θ
() ( )xx
σ
T
θ
逻辑记为
σ
(·)，是一个 sigmoid ...
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