Hello Julians:

I am encountering the issue above when

I am building a logistic regression model

using the GLM package.

I was able to catch a reply from @tim.holy

that covered some functions that could

potentially address this issue HERE

I am not sure what to consider when

attempting to apply the different

PositiveFactorizations.jl functions.

My DataFrame has the structure/content

```
Teams = ["Jazz", "Heat", "Hawks"]
Rank = ["1st", "2nd", "3rd"]
Outcome = ["Win", "Loss"]
#Make sure to add row parameter for EACH attribute (i.e. 50)
Season = DataFrame(Id = 1:50, Gate = rand(50:15:3000, 50),
Top3 = rand(Teams, 50),
Position = rand(Rank, 50),
Column = rand(Outcome .=="Win", 50))
```

I performed the _onehot function from:

```
begin
function _onehot(df,symb)
copy = df
for c in unique(copy[!,symb])
copy[!,Symbol(c)] = copy[!,symb] .== c
end
return(copy)
end
end
```

Then when I attempted perform the logistic regression build

via:

```
fm = @formula(Column~ Top3 + Position + Gate + Jazz + Heat + Hawks + 1st + 2nd + 3rd+
Win + Loss)
logit = glm(fm, train, Binomial(), Probit())
```

I am returning the error in the subject line.

YES! I understand that not using the encoded columns would

yield a result. But am curious what I needed to do to build a

logistic regression WITH the encoded columnar values.