Logistic regression on R - faillll
^Xiaobai, one of the dogs that my family is interested in adopting from ASD!Today I thought I try logistic regression to find the variables that might predict whether a dog gets adopted. (Using the same dataset as my previous post, from kaggle)
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| dataset = read.csv ("train.csv", header=TRUE, na.strings=c("")) | |
| dog = subset(dataset, AnimalType == "Dog") | |
| dog = subset(dog, OutcomeType!="Return_to_owner") | |
| #add column to check if adopted or not | |
| for (i in 1:nrow(dog)) { | |
| if (dog[i,4] == "Adoption") | |
| dog[i,"adopted"] = TRUE | |
| else | |
| dog[i,"adopted"] = FALSE | |
| } | |
| #remove columns that I don't want in the model | |
| dog_filtered = dog[-c(1:3,5,6)] | |
| #logistic regression | |
| model <- glm(adopted ~.,family=binomial(link='logit'),data=dog_filtered) | |
| summary(model) | |
| #model did not converge, too many breeds and color | |
| levels(dog_filtered$Color) | |
| #returned 365 results | |
| levels(dog_filtered$Breed) | |
| #returned 1380 results |
But it failed terribly. R returned a message "model did not converge". I should have realised it when I checked the levels for color and breed and both returned me with over 100 unique values.
This is a screenshot of the results returned. super fail ):
The next step forward for me is to create columns that indicates whether that color of the dog is present. For instance, I will have a column for white, black, brown. If a dog is white/black, it'll have "1" under white and black columns and "0" under brown.
PS: I'm really taking baby steps with this blog!
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