- You can, for example, build a diagram with the categorical values on the x axis and the numerical values on the y axis; any correlation would be clear from this diagram.
- It should be easy for the reader to build diagrams and understand the relationship between variables.
- No. It means that when a variable increases, the other variable decreases.
- This formatting was used in Chapter 6, Data Mining Models in Excel Hands-On Examples.
- We calculated the Squared Error (SSE) as ([@mpg]-[@prediction])^2. The other sum we need is SST = ([@mpg]-average([@prediction]))^2. Then, we calculate R2 = 1-SSE/SST.
- You can try using an exponential function (EXP()) or another function with a similar shape. The R2 value will probably still be far from 1, since the dispersion in the data is very high.
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You're reading from Hands-On Machine Learning with Microsoft Excel 2019
Julio Cesar Rodriguez Martino is a machine learning (ML) and artificial intelligence (AI) platform architect, focusing on applying the latest techniques and models in these fields to optimize, automate, and improve the work of tax and accounting consultants. The main tool used in this practice is the MS Office platform, which Azure services complement perfectly by adding intelligence to the different tasks.
Julio's background is in experimental physics, where he learned and applied advanced statistical and data analysis methods. He also teaches university courses and provides in-company training on machine learning and analytics, and has a lot of experience leading data science teams.
Read more about Julio Cesar Rodriguez Martino
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Julio Cesar Rodriguez Martino is a machine learning (ML) and artificial intelligence (AI) platform architect, focusing on applying the latest techniques and models in these fields to optimize, automate, and improve the work of tax and accounting consultants. The main tool used in this practice is the MS Office platform, which Azure services complement perfectly by adding intelligence to the different tasks.
Julio's background is in experimental physics, where he learned and applied advanced statistical and data analysis methods. He also teaches university courses and provides in-company training on machine learning and analytics, and has a lot of experience leading data science teams.
Read more about Julio Cesar Rodriguez Martino