Question

One machine learning technique is splitting to make predictions. Imagine that the root of a tree is split into branches and that those branches are split into branches, with each split being homogeneous. There are different approaches to splitting a decision tree in Python, depending on the criteria and algorithm used. Some of the common methods are the following: Reduction in variance: This method is used for regression problems, where the goal is to minimize the variance of the target variable in each node. The best split is the one that reduces the most variance after splitting. Information gain: This method is used for classification problems, where the goal is to maximize the information gain or decrease the entropy of the target variable in each node. The best split is the one that increases the most information gain or decreases the most entropy after splitting. Gini impurity: This method is also used for classification problems, where the goal is to minimize the Gini impurity of the target variable in each node. The best split is the one that reduces the most Gini impurity after splitting. Chi-square: This method is also used for classification problems, where the goal is to test the independence of the target variable and each feature in each node. The best split is the one that has the highest chi-square value after splitting. These methods have different advantages and disadvantages, and there is no definitive answer to which one is the best. It depends on the data, the problem, and the preference of the user. Choose 1 of the splitting approaches to decision trees, and implement an algorithm. Identify test data to be used on the algorithm, and illustrate the output of the prediction. Submit 1 Python code file of 100-200 lines and 1 Word document (alternative: Jupyter Notebook that contains the description, code, comments, and results in a single file). • Code must be well commented. Explain your decisions and what the code is doing. Code file must include a file header that includes the following information at a minimum: ○ Your name Date Course Description of the code Code should adhere to best practice code standards. Capture and record results and screenshots in a Word document (alternatively, you can submit a Jupyter Notebook that includes everything). To help you complete the assignment, review the following resources: The CALIPSO Version 4 Automated Aerosol Classification and Lidar Ratio Selection Algorithm Structure-Texture Image Decomposition-Modeling, Algorithms, and Parameter Selection Please submit your assignment.

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