@@BusinessScience haha I love these videos 😅😅😅. You make R language as easy as ABC. I couldn't find anyone on RUclips to create such easy and cool content. Keep it up 👏👏👏
The Decision Tree starts with 100% of the observations (all cars). Then the cars are devided into two groups. In the first example the group on the right are all cars with cty>=16 (these are 59% of all cars). The group on the left are all cars with cty < 16 (41% of all carts). Hope that helps.
Wow I love these amazing videos. Please more videos like that 🤯🤯🤯
Haha! I think I struck a chord with you. Glad you enjoyed it. I have another one coming in 1 week. =)
@@BusinessScience haha I love these videos 😅😅😅. You make R language as easy as ABC. I couldn't find anyone on RUclips to create such easy and cool content. Keep it up 👏👏👏
@@findthetruth3021 Awe! Thanks a lot for the support. I'm glad you are growing. R is fantastic and will help you 10X your career.
Thanks for sharing!
Very nice.
Thank you!!
This is awesome, thanks for sharing!
You got it!
Thank you so much!!!!!!!!!!!!!!very helpful!!!!
You’re very welcome 😊
Hello:) I was just curious was the percentages were in the boxes in the decision tree starting with 100% on top?
The Decision Tree starts with 100% of the observations (all cars). Then the cars are devided into two groups. In the first example the group on the right are all cars with cty>=16 (these are 59% of all cars). The group on the left are all cars with cty < 16 (41% of all carts). Hope that helps.
A cool trick, very helpful
Thanks
You got it!! 😄
I don't understand how you were able to determine that city and fuel economy were highly correlated from looking at the decision tree?
I think its more of an implied correlation
Where can I get the code?