Warning: glm.fit: algorithm did not converge [duplicate] - r

Question

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Why am I getting "algorithm did not converge" and "fitted prob numerically 0 or 1" warnings with glm?
(3 answers)
Closed 2 years ago.
banking_data
library(MASS)
library(randomForest)
Bdata <- read.csv("banking_data.csv")
head(Bdata)
Bdata$y <- ifelse(Bdata$y == "y", 1, 0)
intercept_model<- glm(y~1,family = binomial("logit"),data=Bdata)
summary(intercept_model)
(exp(intercept_model$coefficients[1]))/(1+exp(intercept_model$coefficients[1]))
I tried to run this code but it shows Warning:
glm.fit: algorithm did not converge
It shows :
Call:
glm(formula = y ~ 1, family = binomial("logit"), data = Bdata)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.409e-06 -2.409e-06 -2.409e-06 -2.409e-06 -2.409e-06
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 1754.75 -0.015 0.988
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 0.0000e+00 on 41187 degrees of freedom
Residual deviance: 2.3896e-07 on 41187 degrees of freedom
AIC: 2
Number of Fisher Scoring iterations: 25

Answer 1

Without more information on your data it will be hard to help you.
For general advise, see this existing post.
It could be because of the absence of two classes on your y data. Indeed, for instance :
y <- c(rep(1, 1000))
df <- data.frame(y = y)
reg <- glm(y ~ 1, data = df, family = binomial("logit"))
reg
Will return the same error. Did you check the balance of y with the function table(df$y) ?
Another solution is to increase the maximum of iteration :
reg <- glm(y ~ 1, data = df, family = binomial("logit"), maxit = 100)
reg
However this solution is not useful if you have only no inside your dataset.