The difference between ensemble learning and traditional machine learning

The difference between ensemble learning and traditional machine learning lies in different learners, different training samples and different degrees of dependence.

First, individual learners are different.

1, comprehensive learning: comprehensive learning All individual learners are not homogeneous or heterogeneous.

2. Traditional machine learning: All individual learners in traditional machine learning are homogeneous or homogeneous.

Second, the training samples are different.

1. ensemble learning: the training samples of ensemble learning are obtained by distance sampling.

2. Traditional machine learning: The training samples of traditional machine learning are obtained by random sampling.

Third, the dependence is different.

1. Integrated learning: Weak learners of integrated learning are dependent and cannot be generated in parallel.

2. Traditional machine learning: Weak learners of traditional machine learning have no dependence and can be generated in parallel.