Python Programming Series Part-24. Decision Tree (Non-Linear Classifier)

In this session, a Non-linear binary classifier called Decision Tree (DT) is discussed. The session includes theoretical background on DT along with a demonstration using Google Colab. The session starts by giving an introduction to Decision Tree as a classifier. The Skiing Dataset example from Stanford lecture notes (https://www.youtube.com/watch?v=wr9gUr-eWdA&t=210s&ab_channel=StanfordOnline) is used to introduce a non-linear classification problem. A simple Loan_approval example with three attributes (Credit score, Income & Debt-to-Income Ratio) and two labels (Loan approved & Loan rejected) is used to explain the DT algorithm. For the demonstration, Google Colab is used, the loan_approval dataset (.CSV file) from Kaggle is used and read using Pandas library functions, and the DT model is constructed, Trained & Tested using the Sklearn library. A brief explanation of the Gini index and its computation is given. Link to my Python programming playlist: https://youtube.com/playlist?list=PLoQEwr1U9ota-y0dvm9z97F-vZPLAIV_N&si=Ln45ghu_QUsFMy04