Welcome to this short introduction to
classification models. What are
classification models? In machine
learning there are many different models
all with different types of outcomes.
Classification models are machine
learning models that predict a class
type outcome. In other words a
classification model predicts any kind
of category or class such as apples and bananas.
A classification model uses
attributes of a person or any kind of
entity to predict the entities class, for
example Class A might be apples and
Class B might be bananas. The attributes
of apples and bananas could be their
shape their dimensions and their color.
These data points could be used to
predict the class outcome of it 
likely being an apple or a banana
differentiating apples from bananas
based on their own unique attributes
this means the model learns that 
certain attributes belong to a certain
categories or classes for example 
if it's colored yellow is six to eight
inches long one to two inches wide and
it's crescent-shaped then these
attributes are more likely to belong to
a banana than an apple.
The model makes a prediction that given these attributes the fruit is likely to be a banana
similarly if it purrs has fur and
whiskers and is found in every corner of
the internet then it's likely a cat; if a
croaks, has feathers and wings, and is found
on farms, it is likely a rooster. A
classification model learns that these
attributes belong to a certain
categorical outcome in a supervised way
where it directly maps the
data points to a class label.
The class label can be binary such as positive 
or negative, whether a disease is present or not,
whether the customer is a returning
customer or not, or whether the job
applicant was a success or fail or the
class label could be multiple classes
such as easy, intermediate, and advanced level
in a game, or all types of fruits from
peaches, oranges, and kiwi,
not only apples and bananas
some key algorithms used in 
classification models include decision trees,
naïve bayes, support vector machines,
and neural networks, which you can learn
about these in future videos. They all
take different approaches to predicting
a class outcome. And that quickly sums up
classification models for you! Thanks for
watching, give us a like if you found
this useful, or you can check out our
other videos at Data Science Dojo Tutorials.
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