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* Added Pytests for Decission Tree Modified the mean_squared_error to be a static method Created the Test_Decision_Tree class Consists of two methods 1. helper_mean_squared_error_test: This method calculates the mean squared error manually without using numpy. Instead a for loop is used for the same. 2. test_one_mean_squared_error: This method considers a simple test case and compares the results by the helper function and the original mean_squared_error method of Decision_Tree class. This is done using asert keyword. Execution: PyTest installation pip3 install pytest OR pip install pytest Test function execution pytest decision_tree.py * Modified the pytests to be compatible with the doctest Added 2 doctest in the mean_squared_error method For its verification a static method helper_mean_squared_error(labels, prediction) is used It uses a for loop to calculate the error instead of the numpy inbuilt methods Execution ``` pytest .\decision_tree.py --doctest-modules ```
The Algorithms - Python
All algorithms implemented in Python (for education)
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