Good day everyone in this segment we're going to go in to ensemble learning methods.
Let's start with the first lecture which is on the introduction of this very unique methods which combine
multiple machine learning models together.
Now the file can be accessed from the share folder and basically it will provide you with the links
on how to access this.
Starting with this is that it's on the ensemble learning methods.
It's really based on civil based learning.
And the idea here is that you use a group of predictors can be classifiers can be regressive.
The ultimate aim here is to combine many different types of machine learning models so that they each
complement each other.
So the ensemble methods here there are a few types.
There is the first part would first type which is called bootstrap aggregation more commonly known as
bagging bootstrap sampling really is what we call a sampling with replacement replacement.
Basically means that the are the samples they've used to perform your training get placed back into
the whole pool before you draw another sample out how this works is that combine by averaging the output.
This is for the regresses all combined by voting.
This is in the case of classifications or this is really how begging actually works.
Now this can be applied to many classifiers which includes an which is artificial neural network.
We've also looked at card which is decision tree really.
So that's bootstrap aggregating all bagging the second in clusters is sampling without replacement.
This is core pasting for all these provided links.
So you can actually click and come over to understand and study more.
Should that interest you now boasting is to train week classifiers.
What happened is that you add them to a final strong classifier by waiting.
Now the waiting is by accuracy.
Typically once you've added the data is then re weighted again and again and again the misclassified
samples will gain more weight the correct lead classify samples will lose weight.
There are some exceptions but the essential idea here is that where we make mistakes much like how human
learn where we XY and strong in all of this certain so-called topics that we're not good at it will
place more weight there so that the algorithm keep learning or place more emphasis on learning that
portion so the essence here really is that the algorithm are forced to learn more from the misclassified
samples.
So that's really boasting boosting the links to the next one is Core stacking.
It's also a core stack that generalisation now from Cagle the definition comes this way combine information
from multiple predictive models to generate a new model.
So it's again it's on.
That's why it's called ensemble.
Because you're combining multiple of them.
Oftentimes when the stacked model or also core second level model will outperform each of the individual
models due to its smoothing nature and ability to highlight each base models where it performs best
and discredit each based model where it performs poorly.
For this reason taking is most effective when the base models are significantly different.
Training a learning algorithm to combine the predictions of several other learning algorithms.
Now step one is really train your normal machine learning algorithm just like usual.
Then you actually have a step two here which is called the combine.
Our goal is train using algo predictions from step 1.
So it is actually hence the term stacked.
There are other ensemble methods.
There's Bayes optimal classifier there's Bayesian parameter averaging model combining and bucket of
models I also releases a few others.
There's one final part that I want to highlight instead ensemble methods works best with independent
predictors if they're all the same then there's really not much value to it.
It's a little bit of diversification effect here and it's best to utilize different algorithms to get
the most all of ensemble methods.