This is an old video I made on decision trees using scikit-learn (python) for a course back in 2019 that I am making public now.
Code here: https://github.com/mGalarnyk/Python_Tutorials/tree/master/Sklearn/CART
Video based on this blog: https://medium.com/p/9663d683c952
09:31: Train Test Split (TrainTestSplit.ipynb)
18:17: Decision Tree Exercise with Titantic Data (ExerciseDecisionTree.ipynb)
18:52: Solution to Decision Tree Exercise with Titantic Data (ExerciseDecisionTreeSolution.ipynb)
19:18: Arrange Data into Features Matrix and Target Vector (ExerciseDecisionTreeSolution.ipynb)
21:02: Split Data into Training and Testing Sets (ExerciseDecisionTreeSolution.ipynb)
21:12: Fit a Decision Tree on the Titantic Dataset (ExerciseDecisionTreeSolution.ipynb)
21:56: Make Predictions on the Testing Set and Calculate the Accuracy (ExerciseDecisionTreeSolution.ipynb)
22:10: Compare the Testing Accuracy to the Null Accuracy (ExerciseDecisionTreeSolution.ipynb)
23:38: Confusion Matrix of Titanic Predictions (ExerciseDecisionTreeSolution.ipynb)
24:14: Feature Importance Metric from Decision Trees (ExerciseDecisionTreeSolution.ipynb)
24:52: Creating a Decision Tree Visualization using Matplotlib and Graphviz (ExerciseDecisionTreeSolution.ipynb)
