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Previous models are very popular and are widely used by analysts and data scientists.

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A model based on a simple decision tree is very easy to interpret.

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And it gives very clear decision points which can be used in making business decisions.

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Although a simple decision tree lacks a little bit in terms of accuracy.

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But there are variants or advanced techniques based on decision trees, which we will discuss in the

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later part of the course using which we can increase the accuracy really significantly.

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So if interpretation is your goal, that is you're presenting a concept to people who are not very enthusiastic

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about numbers and complex mathematical models.

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You should use a simple decision tree, which we will learn first in this.

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If prediction accuracy is the goal.

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And you can let go of some of the interpretively of the modern.

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We must use the advanced techniques that we are going to learn in the later part of Diggles.

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OK, so what is our decision tree in a decision tree?

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We are trying to split or segment the population into different parts or regions.

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And each region has a certain set of characteristics of deep predictor variables.

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So in this diffidently.

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We are getting finally four regions.

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Which are classifying each person in two unfit outfit.

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This forest region, which is classifying a person into untracked category, is having two characteristics.

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The person which belongs to this region has aged less than 30 and it's a lot of people.

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Similarly, for second region ages, there's dentally.

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But that person is not eating a lot of people's.

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So in this way.

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Ah!

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Aim is to divide the population into several regions and each region will have a certain collective

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stick of deep predictor variables.

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Let me give you an example to explain it further.

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Suppose you're trying to predict scores of students besides the number of hours they have studied prior

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to the exam.

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And they're scored in the midterm exams.

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We have the data of these 10 students and this, David.

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The first column contains the score that they actually scored in the final exam.

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Second column has the number of artist studied and third column as the midterm score of the student.

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Using this data of Penn students for eleven students, I want to predict this code, given that the

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number of hours that he has studied and the midterms code, he has school.

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So if we want to build a decision entry, we want to split this data into regions.

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If I separate those two things on the basis of number of are studied, that is students who've studied

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less than 10 hours can be one group.

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And students who study in more than 10 hours prior to the exam can be another group.

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Do we see any major difference in this sort of these two groups?

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It turns out that there is a major difference in the average score of these two groups.

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Students who study less than 10 hours on an average score, thirty nine marks, whereas students will

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study more than 10 hours score 75 marks on an average.

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So the first step of this entry, you can see these 10 student had an average score of 57, which is

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written here.

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But this is the entire population.

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That is why it is written as a hundred percent.

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When I split this population using the Rs variable and check whether the Rs study is less than 10 hours

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or more than 10 hours.

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I get this decision.

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If us today is less than 10.

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Then we have this left part in this left part.

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Average school student is 39.

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And it contains 50 percent of the population.

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Since we have 10 students, five students are coming in this side of the tree.

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That is, they have studied less than hours.

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And the other side.

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We have these students were studied more than 10 us.

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For them, the average score is seventy five marks.

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And they had also the population is 50 percent.

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So 50 percent of these two gentlemen left, right, and 50 percent are indeed 80.

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Next, if I look at the midterms score of these students, I can further separate this region or this

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class of student.

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So students who have scored less than sixty five marks on an average scored 70 marks in the final exam.

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Which is 30 percent of the total population.

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In other words, trees to rent belong to this class.

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And if I look at the other Dutch students who have scored more than 65 months in the meter on an average

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score, eighty two marks in the final exam.

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This class has 20 percent of the total population, which is two students.

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Now, if I add more predictive variables in my problem.

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And continue making these blit.

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We get something which resembles an inverted tree.

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This is why such a model is called a decision tree.

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You can see how easy it is to interpret this visual representation.

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And also, it is giving us some clear, actionable insight.

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So if you want to score more in the exams.

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Definitely study more than Danas and also try to get more than 65 marks in your midterm exam.

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Now, let us see what are the different types of visionaries, so like in machine learning models?

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We have two types of models classification indignation in D.C. and also.

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We have two types, regression trees and classification trees.

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So indivisible, you are predicting, is a quantitative type of variable like height of a person or

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number of prospective customers of your business.

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Then we be regression, please.

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Whereas if the variable is categorical, such as will a player score a goal in the football match?

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What does a patient have, heart disease?

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This is several reports for such problems.

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We drop classification 3s.

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We will be discussing both of these types of trees in our course, but we will discuss reignition trees

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first.

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Before we move further, it is important that we take note of important terminologies related to decision

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entries.

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Foster's root node, the first node which contains the entire population, which we want to subdivide

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into regions, is called the root node.

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Root node has a hundred percent of the population.

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Then there is the action of splitting, splitting means dividing a region into subregions.

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So when we divide this group, node bases us, we are performing an action of splitting.

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Then is a decision node.

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Every node where we perform splitting is called a decision node.

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For example, the root node is also a decision node.

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Then when we take a decision here that we are going to split this.

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This is the midterms code of student.

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This is also a decision node.

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Then we have Leif and we lodes.

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These are the last nodes beyond which we do not split any further.

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So here the second, sixth and seventh Nord, we are not split further, the are the live or determine

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load.

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Then there is a subtree, a small subsection of the entire tree is called a Sabry.

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So if I take out this decision node and these two leaf node in this.

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We are containing a subtree.

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You'll also hear about bearding and take note.

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So whenever we split an old.

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That note becomes the bitter note, and the notes that we get after this break are detailed.

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So this light blue three note is the bitter note.

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And these two, six and seven are detailed note of this better paranoid.

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So we'll be using this terminology as we go along this course.

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Remember this terminology.

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And to you in the next video.