Highlight continued action in this video, I want to talk about some of the methods that were used in
this course of Islamic learning.
So, for example, my thoughts are divided into two major classes.
Castronovo taxonomies generated and generated my thoughts so generated my thoughts are focused on combining
the predictions set up retrain models.
These models are usually trained independently of one another and they examine example our indicators,
how the prediction, how that prediction will be kombai by classifiers are not affected by the fact
that they exist in Istanbul.
In this cause, we'll cover two men generated from letters which are voting and stopping and stacking.
Voting, as the name implies, refers to the technique that allow most of us to vote in order to produce
a single answer similar to how individuals vote in the nation elections.
And the most popular, most voted for answer is selected as the winner stacking, on the other hand,
refers to the Medhurst not utilizing a model the might not learns how to best combine based on predictions
on double stacking in-house.
The generation of a new model it does not affect us does is a non generated method.
They generated Mattos, on the other hand, are able to generate and affect the baseline that they use,
they can add to their learning algorithm or dataset to train them in order to ensure diversity and high
performance.
Furthermore, some algorithms can induce randomness in models in order to further enforce diversity.
The man generated Matthau's that will cover these costs are McCain boosting and random forest boosting
technique, mainly targeting my -- modot is main idea is to sequentially generate motos.
Chuch, not hish new model addresses by ISIS in Iran in the previous model, not by iteratively correcting
previous errors, the final example has significantly low blow by us.
Maggie aims to reduce barriers, thus making our room resembles a success of the training that had great
many individual and diverse data set are originating from the same day.
Doesn't matter what separated separate model, a strain on a symbol that desire forcing diversity between
the Istanbul models.
Finally, random forest is similar to backing in that it resembles from the training that I set is that
of sampling instances, assemblies, features, thus creating even more diverse stories as feature strongly
correlated to the target may be absent in many trees.
And that is all in this video.
So I hope you enjoy it and I will see you in the next video.