In the shape of a tree structure, a decision tree constructs regression or classification models. It incrementally cuts down a dataset into smaller and smaller sections while also developing an associated decision tree. A tree with decision nodes and leaf nodes is the end result.
One of the most widely used and useful models for supervised learning is the Decision Tree. It can be used to tackle both regression and classification problems, albeit the latter is more widely utilised. There are three sorts of nodes in this tree-structured classifier.
The key distinction between the random forest method and the decision tree algorithm is that decision trees are graphs that depict all possible outcomes of a decision using a branching strategy. The random forest algorithm, on the other hand, produces a set of decision trees that work in accordance with the output.
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Rishu Shrivastav
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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
5
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
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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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Neel Khairnar
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Kushal is very good explainer he is covering all topics nicely 👍
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