Logistic回归是一种分类模型，其中响应变量是分类的.它是一种来自统计数据的算法，用于监督分类问题.在逻辑回归中，我们寻求找到向量和β;以下公式中最小化成本函数的参数.
$$ logit(p_i)= ln \ left(\ frac {p_i} {1 - p_i} \ right) = \beta_0 + \ betaa_1x_ {1，i} + ... + \ betaa_kx_ {k，i} $$
以下代码演示了如何拟合逻辑回归模型在R.我们将在这里使用垃圾数据集来演示逻辑回归，与朴素贝叶斯相同.
从预测结果的准确性来看，我们发现回归模型测试集的准确率达到92.5％，而朴素贝叶斯分类器的准确率为72％.
library(ElemStatLearn) head(spam) # Split dataset in training and testing inx = sample(nrow(spam), round(nrow(spam) * 0.8)) train = spam[inx,] test = spam[-inx,] # Fit regression model fit = glm(spam ~ ., data = train, family = binomial()) summary(fit) # Call: # glm(formula = spam ~ ., family = binomial(), data = train) # # Deviance Residuals: # Min 1Q Median 3Q Max # -4.5172 -0.2039 0.0000 0.1111 5.4944 # Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -1.511e+00 1.546e-01 -9.772 < 2e-16 *** # A.1 -4.546e-01 2.560e-01 -1.776 0.075720 . # A.2 -1.630e-01 7.731e-02 -2.108 0.035043 * # A.3 1.487e-01 1.261e-01 1.179 0.238591 # A.4 2.055e+00 1.467e+00 1.401 0.161153 # A.5 6.165e-01 1.191e-01 5.177 2.25e-07 *** # A.6 7.156e-01 2.768e-01 2.585 0.009747 ** # A.7 2.606e+00 3.917e-01 6.652 2.88e-11 *** # A.8 6.750e-01 2.284e-01 2.955 0.003127 ** # A.9 1.197e+00 3.362e-01 3.559 0.000373 *** # Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 ### Make predictions preds = predict(fit, test, type = ’response’) preds = ifelse(preds > 0.5, 1, 0) tbl = table(target = test$spam, preds) tbl # preds # target 0 1 # email 535 23 # spam 46 316 sum(diag(tbl)) / sum(tbl) # 0.925
