3
No, because variables can be correlated. For an exemple you can take a degenerate problem where you have the same variable twice. Their IV will be the same by construction, however their coefficient could vary wildly as long as their sum is constant and equal to the coefficient you would get with one variable. So even with high IV you can get pretty much ...
2
In general, there is no context-free number that is 'too large'. It can be that some values don't make sense, given what they mean, though. If numbers seem too large for the context, one thing to consider for models with categorical outcomes is separation. If that happened, you'd typically have very large slopes, but also very large standard errors (which ...
1
Here's what I think is producing the pattern you see.
Models are approximations of $\Pr(Y=1|X) = E[Y|X]$ using data $Y, X$. Compared to unregularised logistic regression, machine learning models typically do a much better job of that, judged by mean squared error, log loss etc, when $X$ is high dimensional because they are not as likely to pick up spurious ...
1
I realized I fell for the dummy variable trap, creating variables with perfect multicollinearity. So initially, I tried
X = df.drop('solved',axis=1)
y = pd.DataFrame(df['solved'])
X = sm.add_constant(X)
model = sm.Logit(y,X)
result=model.fit()
print(result.summary2())
which results in
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