Think of bias as Sakura’s early Naruto skills - basic, predictable, but kinda weak. A high-bias model (like Sakura punching air) makes oversimplified assumptions.
For example, using a straight line (y=mx+by = mx + by=mx+b) to fit data that’s clearly a curve.
Result? Underfitting - your model’s as useful as a screen door on a submarine.
On the flip side, variance is like Nine-Tails Naruto: chaotic, unpredictable, and way too extra.
A high-variance model memorizes the training data’s noise (like Naruto’s rage fits), leading to overfitting.
It’s great on paper but flops in real life - like predicting your crush will text “👀” because they did once during a full moon.
The total error in your model can be split into three parts: