To warm up, here are some good quotes.

Happiness is a place between too little and too much. ~ Finnish Proverb

Enough is as good as a feast. ~ English Proverb

Don’t be sweet, lest you be eaten up; don’t be bitter, lest you be spewed out. ~ Jewish Proverb

To go beyond is as wrong as to fall short. ~ Confucius

Now let’s get into the topic of data science.

Trade off of bias vs. variance

This would be the first thing to think of before an modelling.

Side note to myself:
Bias, likely for underfitted model, and has similar high model errors for both training and testing set.
Variance, likely for overfitted model, that fits really well on training set, but not testing. the variance refer to the variance between fits.

Regularization is there to help peeps to make the tradeoff by penalizing the model.

Model selection

Another balance, trade off to be considered. We usually starts with lm and may end up there too sometimes. Or else try out the complex models, or doing aggregates such as bagging, boosting.