Decomposition Tree Visual in Power BI- Part Two

FacebooktwitterredditpinterestlinkedintumblrmailFacebooktwitterredditpinterestlinkedintumblrmail
FacebooktwitterlinkedinrssyoutubeFacebooktwitterlinkedinrssyoutube

In the last blog an introduction to the Decomposition tree has been provided.

In this blog, AI split of the decomposition tree will be explained.

From last post, we find out how this visual is good to show the decomposition of the data based on different values.

There is another split based on the how other values has impact on the root data.

In this blog I will explained it using two different dataset, the one that we have from previous blog and another one that is about the insurance data.

we can split the data based on what has more impact on the analyse value

How it is work?

 

AI Split

AI Slit is a feature that you can enabl;e or disable it.

imagine we have a dataset about insurance charges regarding the Gender, age BMI people smok or not number of children they have and so forth.

So start from importing the dataset into Power BI desktop and add the Decomposition tree to the report with analyse of Charges to be explained by Age, Gender, BMI, and so forth

In the next satep, we have the parent node of the sum of insurance charges as below. If you click on the plus sign st the top of the menue you can see High Value and Low Value with Lamp sign,

What is their definition?

High Value 

High value refer to drill into which variable ( age, gender…) to get to get the highest value of the measure being analysed[resource ]. Or in a simple way which of these variable has impact the insurance charges to be higher! t is so similar to  correlation analysis to find out which factor has more impact to have higher charges

Low Value

Low value refer to drill into which variable ( age, gender…) to get to get the lowest value of the measure being analysed[resource ]. Or in a simple way which of these variable has impact the insurance charges to decrease! it is so similar to  correlation analysis to find out which factor has more impact to have lower charges

So in this example we find out the Gender of people has impact

Some notation

we do not Choose Sex to be selected, based on the algorithm the next level that has more impact on the charges to be hight is Sex of people.

Now in another analysis I want to know which of them decrease the amonth of charges. I remove the previous one and add the low value, as you can see in the below picture, BMI of people has impact to have lower charges peple with BMI 15, 20… has lower charges.

Now, you can have combination of them, I remove the second level and choose the High value again

 

So for charges to be Hight, if they are Men (charges with sum of 9 Million) and if they smoke (that is 5 Million) they have to pay more for insurance  charges

 

lets try other scenario : for a Men need to pay higher charges, but if the men with BMI of 21,20,17 and even 31 the charges would be low!

 

You can analyse it in different ways,

In next Blog, I will explained how to enable and disable AI Split and how to implement the  relative and absolute concept

see below video

 

FacebooktwitterredditpinterestlinkedintumblrmailFacebooktwitterredditpinterestlinkedintumblrmail
FacebooktwitterlinkedinrssyoutubeFacebooktwitterlinkedinrssyoutube
Leila Etaati on LinkedinLeila Etaati on TwitterLeila Etaati on Youtube
Leila Etaati
Trainer, Consultant, Mentor
Leila is the first Microsoft AI MVP in New Zealand and Australia, She has Ph.D. in Information System from the University Of Auckland. She is the Co-director and data scientist in RADACAD Company with more than 100 clients in around the world. She is the co-organizer of Microsoft Business Intelligence and Power BI Use group (meetup) in Auckland with more than 1200 members, She is the co-organizer of three main conferences in Auckland: SQL Saturday Auckland (2015 till now) with more than 400 registrations, Difinity (2017 till now) with more than 200 registrations and Global AI Bootcamp 2018. She is a Data Scientist, BI Consultant, Trainer, and Speaker. She is a well-known International Speakers to many conferences such as Microsoft ignite, SQL pass, Data Platform Summit, SQL Saturday, Power BI world Tour and so forth in Europe, USA, Asia, Australia, and New Zealand. She has over ten years’ experience working with databases and software systems. She was involved in many large-scale projects for big-sized companies. She also AI and Data Platform Microsoft MVP. Leila is an active Technical Microsoft AI blogger for RADACAD.

Leave a Reply