Assignment decision trees
In this assignment, we will find out how a decision tree works in a regression task, then will build and tune classification decision trees for identifying heart diseases. Prior to working on the assignment, you’d better check out the corresponding course material: Classification, Decision Trees and k Nearest Neighbors, the same as an interactive web-based Kaggle Kernel Ensembles: Bagging, the same as a Kaggle Kernel Random Forest, the same as a Kaggle Kernel Feature Importance, the same as a Kaggle Kernel Gradient boosting, the same as a Kaggle Kernel Logistic regression, Random Forest, and LightGBM in the “Kaggle Forest Cover Type Prediction” competition: Kernel You can also practice with demo assignments, which are simpler and already shared with solutions: “Decision trees with a toy task and the UCI Adult dataset”: assignment + solution “Logistic Regression and Random Forest in the credit scoring problem”: assignment + solution There are also 7 video lectures on trees, forests, boosting and their applications: mlcourse.ai/lectures
