deepeshdm / Decision_Tree_ID3

A Decision Tree Model applied using the ID3 algorithm , capable of handling both Categorical & Continuous features.

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In order to apply this algorithm efficiently you must follow certain criteria.

1] The last column of the dataset should contain the target / label classes.

2] Also the last column should be named as "label"

The algorithm uses only numpy & pandas as dependencies

Caution : The efficiency of the model is directly related to the number of label classes you have. A large number of label classes also results an exponential growth in data splitting which is computationally expensive.

Formulas used to calculate Entropy :

11 03 2021_10 28 01_REC

11 03 2021_18 20 59_REC

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A Decision Tree Model applied using the ID3 algorithm , capable of handling both Categorical & Continuous features.

License:GNU General Public License v3.0


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Language:Python 100.0%