modeling

Distribution questions check whether you can map a data-generating process to the right model. What matters is knowing what each distribution describes plus 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 depends on your model and what you want from it. This is the answer that tells rote apart from real understanding.

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

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

When labels exist only for the cases you already acted on, the model learns a distorted world: strong offline, blind to everyone you never saw. Worse, its own decisions choose 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 base decisions on that bar chart. Here is why default importance misleads and what a careful answer reports instead.

Most real datasets carry wrong labels, and they quietly cap your accuracy. Hand-cleaning all of it does not scale. Here is how to surface the bad labels and train robustly around them.

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