*“Wisdom of a weighted crowd of experts”*

## Classifiers

Classifiers are tests that produce **binary choices** about samples. They are considered **strong classifiers** if their error rate is close to 0, **weak classifiers** if their error rate is close to 0.5.

By using **multiple classifiers** with different weights, data samples can be sorted or grouped according to different characteristics.

## Decision tree stumps

Aside from classifiers, a **decision tree** can be used to sort positive and negative samples in a 2-dimension space. By adding **weights** to different tests, some samples can be emphasized over the others. The total sum of weights must always be constrained to 1 to ensure a proper **distribution** of samples.

## Dividing the space

By **minimizing the error rate** of the tests from the weights, the algorithm can cut the space to sort positive and negative examples.

## No over fitting

Boosting algorithms seems not to be over fitting, as the decision tree stumps tends to be very **tightly close to outlying samples**, only excluding them from the space.