en_Intro to Decision Trees.txt

Hello, and welcome!
In this video, we’re going to introduce and examine decision trees.
So let’s get started
What exactly is a decision tree?
How do we use them to help us classify?
How can I grow my own decision tree?
These may be some of the questions that you have in mind from hearing the term, decision tree.
Hopefully, you’ll soon be able to answer these questions, and many more, by watching
this video!
Imagine that you’re a medical researcher compiling data for a study.
You’ve already collected data about a set of patients, all of whom suffered from the
same illness.
During their course of treatment, each patient responded to one of two medications; we’ll
call them Drug A and Drug B. Part of your job is to build a model to find
out which drug might be appropriate for a future patient with the same illness.
The feature sets of this dataset are Age, Gender, Blood Pressure, and Cholesterol of
our group of patients, and the target is the drug that each patient responded to.
It is a sample of binary classifiers, and you can use the training part of the dataset
to build a decision tree, and then, use it to predict the class of an unknown patient
… in essence, to come up with a decision on which drug to prescribe to a new patient.
Let’s see how a decision tree is built for this dataset.
Decision trees are built by splitting the training set into distinct nodes, where one
node contains all of, or most of, one category of the data.
If we look at the diagram here, we can see that it’s a patient classifier.
So, as mentioned, we want to prescribe a drug to a new patient, but the decision to choose
drug A or B, will be influenced by the patient’s situation.
We start with the Age, which can be Young, Middle-aged, or Senior.
If the patient is Middle-aged, then we’ll definitely go for Drug B.
On the other hand, if he is a Young or a Senior patient, we’ll need more details to help
us determine which drug to prescribe.
The additional decision variables can be things such as Cholesterol levels, Gender or Blood
Pressure.
For example, if the patient is Female, then we will recommend Drug A, but if the patient
is Male, then we’ll go for Drug B. As you can see, decision trees are about testing
an attribute and branching the cases, based on the result of the test.
Each internal node corresponds to a test.
And each branch corresponds to a result of the test.
And each leaf node assigns a patient to a class.
Now the question is how can we build such a decision tree?
Here is the way that a decision tree is built.
A decision tree can be constructed by considering the attributes one by one.
First, choose an attribute from our dataset.
Calculate the significance of the attribute in the splitting of the data.
In the next video, we will explain how to calculate the significance of an attribute,
to see if it’s an effective attribute or not.
Next, split the data based on the value of the best attribute.
Then, go to each branch and repeat it for the rest of the attributes.
After building this tree, you can use it to predict the class of unknown cases or, in
our case, the proper Drug for a new patient based on his/her characterestics.
This concludes this video.
Thanks for watching!