Fit the logit <- GLM (class ~., data=https://bbs.csdn.net/topics/df.train, family=binomial ())
The summary (fit. Logit)

Call:
GLM (formula=class ~., family=binomial (), data=https://bbs.csdn.net/topics/df.train)

Deviance Residuals:
Max Min 1 q Median 3 q
- 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 1.6 e-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. Mr Pred <- factor (prob>. 5, levels=c (FALSE, TRUE),
Labels=c (" benign ", "malignant"))
Logit. Perf <- table (df) validate $class, logit. Mr Pred, within DNN=c (" Actual ", "Predicted"))
Logit. Perf
Predicted
Actual benign malignant
Benign 118 2
Malignant 4 76