3 votes

Why do discrete choice models (such as MNL) not require test set?

Multinomial logistic regression can and often does consider out-of-sample performance. For instance, LeCun (1998) applied multinomial logistic regression to the pixels of the MNIST handwritten digits, ...
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3 votes
Accepted

What is the intuition behind the odds scale?

In the frequency interpretation, probability is the number of successful shots divided into the total number of shots (at each distance $x$). The odds is the number of successful shots per failure. ...
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2 votes

How do I do a logistic regression model in R for an outcome with multiple values?

When you want to do something like logistic regression but with $3+$ outcome categories instead of two, the $y$ is multinomial instead of binomial. Consequently, the analogous model is multinomial ...
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2 votes

Logistic regression simulation with respect to event occurrence (prevalence)

You have an array of explanatory variables $(x_1, x_2, \ldots, x_n)$ ($n=20000$) and a model that assigns a probability to each $x_i.$ You seek a subarray of these variables that has a mean ...
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1 vote

Is it normal for simple logistic regression to significantly outperform any other statistical ML algorithm?

My informal answer is that maximum likelihood estimation, the method behind logistic regression, finds the set of parameters that fit the data the best given some assumptions. If your dataset ...
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1 vote

Interpreting Results of Logistic Regression when both x, y variables are nominal

Comment: As @Ralph Winters points out, the question "Is the probability of choosing B that same in all four groups?" can be answered by performing a chi-squared test of independence. This ...
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