fit.logit <- glm(class~., data=https://bbs.csdn.net/topics/df.train, family=binomial())
summary(fit.logit)
Call:
glm(formula = class ~ ., family = binomial(), data = df.train)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.7581 -0.1060 -0.0568 0.0124 2.6432
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -10.4276 1.4760 -7.06 1.6e-12 ***
clumpThickness 0.5243 0.1595 3.29 0.0010 **
sizeUniformity -0.0481 0.2571 -0.19 0.8517
shapeUniformity 0.4231 0.2677 1.58 0.1141
maginalAdhesion 0.2924 0.1469 1.99 0.0465 *
singleEpithelialCellSize 0.1105 0.1798 0.61 0.5387
bareNuclei 0.3357 0.1072 3.13 0.0017 **
blandChromatin 0.4235 0.2067 2.05 0.0405 *
normalNucleoli 0.2889 0.1399 2.06 0.0390 *
mitosis 0.6906 0.3983 1.73 0.0829 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
prob <- predict(fit.logit, df.validate, type="response")
logit.pred <- factor(prob > .5, levels=c(FALSE, TRUE),
labels=c("benign", "malignant"))
logit.perf <- table(df.validate$class, logit.pred,dnn=c("Actual", "Predicted"))
logit.perf
Predicted
Actual benign malignant
benign 118 2
malignant 4 76
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