Decision Tree Classifier with Decision Tree Analysis & Example: how it works |Part -2| NerdML

This "Decision Tree Classifier with Decision Tree Analysis & Example" video will help you to understand how can we make use of Entropy, Information Gain & Gini Impurity for building Decision Tree algorithm. We will mathematically solve the classification problem. I have divided Decision Tree tutorial into several parts which will cover basic intuition, Classification problem solving & regression problem solving.

A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Each split corresponds to a node in the. Splitting stops when every subset is pure (all elements belong to a single class) -- this can always be achieved, unless there are duplicate training examples with different classes.

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Please find the previous Video link -
Decision Tree (Basic Intuition - Entropy, Gini Impurity & Information Gain) | NerdML : https://youtu.be/Xfgq8zh8sDQ

Machine Learning Tutorial Playlist: https://youtube.com/playlist?list=PLAH6DbJL1J2KroCzEWRF0xnrmet9esxFH

Deep Learning Tutorial Playlist : https://youtube.com/playlist?list=PLAH6DbJL1J2KK-5PlYCt3v2PaIGdvuLmB
Prerequisites
Basic understanding of Linear Algebra, Probability, Calculus, Matrix & Python programming including pandas, numpy, scikit learn & some visualization tools.

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Creator : Rahul Saini
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Видео Decision Tree Classifier with Decision Tree Analysis & Example: how it works |Part -2| NerdML канала NerdML