modeling

Distribution questions test whether you can match a data-generating process to the right model. The signal is knowing what each distribution describes and a concrete use case, not memorizing PDFs. Here is the practical map.

The textbook reflex ('drop one') is usually the wrong instinct, and whether collinearity matters at all hinges on your model and what you need from it. This is the answer that separates rote from understanding.

Everyone can recite 'linearity and normality.' Few can say which assumption matters for predictions versus inference, how to spot a violation in a residual plot, and what to actually do about it. Here is that answer.

A 92% aggregate accuracy can hide 60% on the segment that matters most. Average metrics are exactly where these failures hide. Here is how to surface them and the menu of fixes that actually map to the cause.

If labels only exist for the cases you already acted on, the model learns a distorted world: great offline, blind to everyone you never saw. Worse, its own decisions pick the next labels. Here is how to spot it and counter it.

The built-in importance scores from XGBoost can rank a random ID above a vital feature, and stakeholders will make decisions on that bar chart. Here is why default importance lies and what a careful answer reports instead.

Most real datasets have wrong labels, and they cap your accuracy invisibly. Cleaning all of it by hand doesn't scale. Here is how to find the bad labels and train robustly around them.

The idea that models, like students, learn better from easy examples first is intuitive, and sometimes it works and sometimes it does nothing. Knowing when it pays off is the real signal. Here is the honest answer.