To decide whether to break a node into two or more sub-nodes, decision trees employ a variety of techniques. The homogeneity of the generated sub-nodes improves with the generation of sub-nodes. To put it another way, the purity of the node improves as the target variable grows.
A decision tree is a tool with a tree-like structure that predicts likely outcomes, resource costs, utility costs, and potential implications. Decision trees are a useful tool for presenting algorithms. They use software to automate trading in order to produce profits at a rate that would be hard for a human trader to achieve.
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Muhammad Qasim
5
Hi Kushal ! Your way of teaching is extremely helpful and you are one of the best teacher in the world.
Extremely helpful and I recommend to my peer as well for this course.
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Shafi Akhtar
5
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Aniket Kumar prasad
5
Very helpful and easy to understand all the concepts, best teacher for learning ML.
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Rishu Shrivastav
5
explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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VIKAS CHOUBEY
5
very nicely explained
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Vrushali Kandesar
5
Awesome and very nicely explained!!!
One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
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Mohd Mushraf
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Amazing Teaching
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Juboraj Juboraj
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Easy to understand & explain details.
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Joydeb
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Awesome Course sir and your teaching style is very GOOD.
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Shaga Chandrakanth Goud
5
Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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