Hi everyone, and welcome to our ninth module,
which will be focused on machine learning in Jupiter and Lea.
To introduce our module,
we will learn the basics about gaming clustering, and we will create, uh,
and evaluate, uh,
cames models and find its optimal number of clusters using, uh, various methods.
Then we'll learn the basics about, uh, XG boost algorithm,
and we'll make a binary model with it.
We will learn also the basics of live G B M and we'll tackle a
multi classification problem.
We'll learn how to effectively evaluate our models, uh,
using cross validation.
We'll also create a whole pipeline consisting of multiple pre
protesting steps and the model.
And since the algorithms and the pre-processing steps can have, uh,
many hyper parameters,
we'll learn how to find the best ones using few methods of hyper parameter
tuning. In this video, we will learn more about,
uh, k use clustering.
It's objective is to partition a set of observations into
a specific number of clusters where each observations belongs to a cluster with
the nearest mean serving as a prototype for this cluster,
this 10 trade based, uh,
that means it works by assigning data points to the next, uh,
centrate and then recalculating centrate based on the points assigned to it.
And this process is repeated until convergence, it's distance based.
That means this calculating the and distance between points, uh, scaling,
uh, is very important. Uh, since it is, uh, distance based,
it is a crucial factor. All variables need to be on a same scale.
Number of clusters need to be specified in advance. Uh,
this might be a disadvantage of this clustering algorithm, uh,
but ca is clustering is used usually, uh, where you have, uh,
domain knowledge to know approximately the number of clusters in advance. But,
uh, there are methods to find suitable number of clusters,
even if you do not know the exact number. It works with numeric data,
uh, because it is distance based. It works, uh,
best with only numeric data like continuous or ordinal variables.
You can, of course,
transform categorical variables into numeric using one hot encoding,
for example. But, but, uh, in case of Cains,
it is not recommended. Uh, so if you can use only numeric variables,
it's, uh, a very scalable algorithm in terms of, uh,
number of rows and also number of variables.
But in the relation to number of variables,
it is highly influenced by the curse of dimensionality. That means,
uh, that with higher number of variables,
the distances between observations tend to become more uniform.
Uh, it's, uh, basically means that, uh,
all points seem are roughly equity distance from one or other.
Uh, in short, the less variables used, the better, uh,
Cains algorithm assumes, uh, clusters to be similar size,
uh, density and spherical.
It may happen that clusters of different sizes will not be identified as well as
clusters, uh, with various densities or with complicated shapes.
Cains algorithm is sensitive to initial conditions.
Different initializations may lead to different results,
and Cains may be stuck in local mini. But, uh,
there are few initialization techniques which help, uh, to tackle this problem.
Uh, it is also very sensitive to outliers. Uh, so, uh,
proper, uh, normalization method is also very important.
It means clustering, uh, it's an, uh, unsupervised national learning method.
Uh,
that means we do not know the group where the observations are belonging in
advance. We assigning the group, uh, or the cluster, uh,
based on similarity of the inputs. In our next video,
we will create a clustering model in Jupiter.