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FKara.3
Associate III
February 20, 2024
Solved

Decision Tree evaluation Parameters in LSM6DSV16x

  • February 20, 2024
  • 1 reply
  • 1288 views

Hello,

 

I have a question regarding the evaluation parameters for decision tree results. As you can see in the picture below, the Mean Absolute Error and Root Mean Squared Error are high, but the accuracy is also high. I am confused because I thought that low error and high accuracy are supposed to go hand in hand. Can you please clarify this for me?

 

FKara3_0-1708462905883.png

 

Kindly,

Fehmi

Best answer by Federica Bossi

Hi @FKara.3 ,

Mean Absolute Error and Root Mean Squared Error are the average accuracy in cross validation. I will point out to the team that the word 'error' can be confusing and to remove it in future releases. Thanks for pointing this out!

1 reply

Federica Bossi
Federica BossiBest answer
Technical Moderator
February 23, 2024

Hi @FKara.3 ,

Mean Absolute Error and Root Mean Squared Error are the average accuracy in cross validation. I will point out to the team that the word 'error' can be confusing and to remove it in future releases. Thanks for pointing this out!

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FKara.3
FKara.3Author
Associate III
March 1, 2024

Hi @Federica Bossi ,

 

Perfect.

I have another question. What about Kappa statistic? Why is it low?

FKara3_0-1709329432613.png