Supervised Learning in Machine Learning > Supervised Learning Algorithms

Decision Trees Practical - 1

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A decision tree is a flowchart-like structure in which each internal node represents a test on a feature (e.g. whether a coin flip comes up heads or tails) , each leaf node represents a class label (decision taken after computing all features) and branches represent conjunctions of features that lead to those class.

A decision tree has the substantial advantage of forcing the evaluation of all conceivable outcomes of a decision and tracing each path to a conclusion. It generates a complete analysis of the effects along each branch and flags decision nodes that require additional investigation.

A decision tree makes its decision by running a series of tests.

The ID3 (by Quinlan) algorithm is the fundamental method utilized in decision trees. The ID3 algorithm constructs decision trees in a top-down, greedy manner.

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Priya Singh
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good

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Rohit Khare
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What will be the mandatory requirement of configuration of PC for this ML tool

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Muhammad Fahad Bashir
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Explained the concept easily

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Pradeep Kumar Kaushik
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Please give me iris,csv file.

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Ankit Malik
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where is the finaldata.csv

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Vimal Bhatt
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great learning plateform kushal sir is really too good

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Prabhat Yadav
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Superb course content and easy to understand.

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fahad ameer
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good

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  1. Introduction to Machine Learning
    1m 5s
  2. Environment Setup part 1
    3m 26s
  3. Environment Setup part 2
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  1. Data Wrangling
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    17m 3s
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    3m 38s
  8. Splitting Dataset - Practical
    9m 36s
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    9m
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    19m 12s
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  6. Decision Trees for Classification
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  7. Decision Trees Practical - 1
    22m 41s
  8. Decision Trees Practical - 2
    20m 42s
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  1. Interview Questions Part 1
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