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.

January 1, 1970

Plant Seedlings Classification

AI saturday(4 month Jeremy Howard training) project Placed 537 out of 3000 parcipaint Can you differentiate a weed from a crop seedling? The ability to do so effectively can mean better crop yields and better stewardship of the environment. The Aarhus University Signal Processing group, in collaboration with University of Southern Denmark, has recently released a dataset containing images of approximately 960 unique plants belonging to 12 species at several growth stages.

Flight delays

Predict whether a flight will be delayed for more than 15 minutes. In this assignment, you’re asked to beat a baseline in the “Flight delays” competition. This time we decided to share a pretty decent CatBoost baseline, you’ll have to improve the provided solution. 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.