A couple questions.<p>&gt; OLS works fine in classification problems. And it has advantages.<p>Do you have more explanation of these advantages? I read through the link you sent, and a bit more about linear probability models. Such things were never discussed in my statistics curriculum (BS, MS, PhD), except for motivating why logistic regression was necessary. I&#x27;m not sure I understand the economist&#x27;s arguments in favor of LPM. Both the interpretation and the distribution of the test statistics will be totally different with OLS versus Logistic Regression, and the overall probability of a defunct project is pretty small ( \hat{P(y=0)} = .07 )--enough where there would be pretty big differences. To be clear, my reservation is with the p-values in the OLS model, not the predictions it generates. While the models agree on the direction of the covariates, the magnitudes are quite different, even when you convert logit&#x2F;probit to be on the same scale as LPM.<p>&gt; Multicollinearity refers to perfect multicollinearity.<p>Perfect multicollinearity will definitely mess up the estimation, but even if Score and Comments are not perfectly collinear, it&#x27;s difficult to talk about each one&#x27;s effect on the probability individually, as is the interpretation of coefficients in a (logistic) regression. What does the VIFs look like for Score and Comments, in particular?<p>&gt; I mention R² as one measure of predictive power.<p>But the outcome is binary, so you&#x27;ll have a similar issue as Minimaxir&#x27;s first point about OLS. If you wanted to talk about prediction accuracy, what about a confusion matrix, misclassification rate, or specificity&#x2F;sensitivity&#x2F;F1? Granted, you&#x27;ll not want to predict on the same tagged examples that you trained the model on, but maybe you could split it 80-20? Or tag another 20-50? There are also R²-like measures you can use when the dependent variable is binary (a whole class of pseudo-R² measures).<p>I would be curious to see the relationship between these predictors and the response. It&#x27;s usually been my experience that linearity is a strong assumption to make, and that I&#x27;d expect for something like comments or score that once it reached a certain threshold, there was no extra value added by getting more comments&#x2F;score. Are the log-score and log-comments linear over their entire support?