Now that we have the both unsupervised learning, let us have a discussion on different types of evaluation
parameters, which we have so clustering is used for.
Grouping different types of data, datapoint.
Now here you can see we have different types of data points present and these have been clustered by
different methods which represent.
These different methods allow to create clusters with different properties.
Now, this particular documentation, which is present inside Gitlow, provide a complete overview of
how each algorithm can be implemented.
So when we go below, we can directly see the implementations like DV scan, hierarchical clustering.
Gaming's.
And rescan.
All of these have been implemented here, and you can see the implementation and details about the same
by going inside the demo for the same.
Here is the entire implementation of this.
This is another documentation for different types of examples, which we have, so here you can actually
find out different examples for classification problems, flustering problems for convenience, estimation.
Different data set examples, decision trees, ensemble methods and different methods which are available,
and there are different exercises also which are available.
So you can try these out and see how good you can work on these.
Next, we will try to have a look at the demonstrations.
So here we have different demonstrations, all of gaming's and different type of algorithms which are
present.
So here we can see the hudud, the clustering, the underground, which has been created here.
And how we can plot this.
Again, you can go to plastering.
And see the.
Implementation of gaming's here.
This has the complete implementation of gaming's with the plot.
For those who can again go up.
And again, have a look at a different clustering algorithms, the demos, the examples, so it has
a lot of information which you can use.
And here is a comparing different clusters and algorithms.
So a lot of examples are present here for your exploration.
This is the website for Psychic Land, which has all these information available.
Then you can go to the user guide, which is present above this, you visit guide contains details of
all the supervised learning algorithms, unsupervised learning algorithms, how we can do modern selection,
how we can evaluate the models.
Lake cross-validation during the hypovolemic, those metrics and scoring model, persistance, validation
of.
How we can do that transformations how we can load different detours so all of these things are given
in this particular Whipsnade, which is very nice and very nicely explained, everything is provided.
So we'll go to clustering here and clustering.
We have this last option, which is clustering performance evaluations, which we go through this.
So we have several metrics which are present for clustering performance evaluation, first one being
the adjusted right index.
It is the for the ground truth, for the class assignment, so are clustering algorithm of language
of the same level and just an index is the function that measures the similarity of the assignments,
ignoring full mutation and with chance normalization.
So it will basically compare the actually deals with the labels, which we have provided and tell us
how good our clustering is.
But this is used when we already have the labels of.
So the advantages are that this particular labelled assignment having at a school close to zero point
zero, so for any value of interested in sample, which is not the case for index and the measure,
so these can be used.
Then there is another index, which is mutual mutual information based school, which can again be found
using Escalon metric DOT adjusted mutual information.
So this is the school which you can actually use.
The perfect labeling school would be one.
Next, which we have, is the homogeneity or completeness and we measure these are different measures
which we have.
So this has the concept of homogeneity, score and completeness for so homogeneity is what gives how
homogeneous the data is, the clusters.
So we can find this out, and the one which is most important and mostly used is the psyllid coefficient
psyllid coefficient is of comparing the mean distance between a sample and all other points in the same
cluster that the mean distance between the sample and all other points belonging to the next nearest
clusters.
So based on these distances, that is the inverted cluster distance and the cluster distance.
It finds out this and it's called the Senate score ranges from minus one to one there.
One is the perfect cluster formation.
Negative one means that the cluster has been misclassified and the zettl means that the clusters are
overlapping in nature.
And here you can again find the implementation of the same.
We will be using this implementation while we will be implementing different models in our lecture's.
So the simple implementation is we will involved the skill on import metric and from the metric, we
will simply use metric dorsolateral.
We will give the values, the labels and the metric that you want to use for the distance calculation,
and it will give the values which should be near two one.
So this is one very important index and most importantly used index, which is political efficient.
So this is what we will be using for this particular obsession and for all the.
Of clustering algorithms, which we will be implementing.
So this is about the Escalon Library and how you can go through this library and take different algorithms
and the examples which are available under this.
So you can go to the examples and see different examples available for all types of problems which are
present.
Similarly, you can go to the user guide and under User Guide, you can look up any algorithm which
you want to find about and learn about it next in case you want to learn about the EPA itself.
You can simply go to the EPA and it will start showing the the methods and different usually used libraries
from this particular EPA.
So this is about Escalon and how we can actually use different coefficients and most importantly, Sillett
index for finding out the goodness of a cluster.
So thank you.
In the next session, we will be learning about Haralson clustering and then we will talk about different
clustering methods which are available.