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I never use Linear Regression! I am not saying you shouldn’t, I personally just never do. Actually there are many algorithms I never use: Logistic Regression, Naive Bayes, SVM, LDA, KNN, Feed Forward Neural Network,… I just don’t find value in those for my work. I always say you should start any Machine Learning model development with a simple algorithm, but for me Logistic Regression (LogReg) or Linear Regression (LR) are not simple! The amount of feature engineering needed to get a performant model out of those algorithms is just too high to me. The simple use of those algorithms does not provide me with a useful minimum baseline I could work from. If I observe low predictive performance on a LR or LogReg with zero feature engineering, that is not informative enough for me to make a predictive power assessment of the underlying data. For me the “simplest” model to use is XGBoost (this includes LightGBM or CatBoost of course). Well at least for tabular data. XGBoost natively handles missing values, categorical variables (to some extent!), it is parallelizable (so fast…enough), it trains on any loss function I may need, it tends to be robust to overfitting on a large number of features, and it is highly non-linear which makes it a very low bias algorithm. Its API is pretty complete, and you can achieve a lot by just changing a couple of arguments. I can easily throw any data into XGBoost without any work and that gives me a useful baseline that can drive my development from there.