ProjectBeta
Calculate Metrics with Pen and Paper
Easy
10 completions
~ 6 hours
4.3 This content is new. Please help us improve it by reporting bugs if you encounter them.
You will learn how to calculate various metrics for classification and regression tasks and how to interpret them. The project requires only basic math and absolutely no coding, only a pen and a sheet of paper!
Provided by
JetBrains Academy
About
When training a machine learning model, it's vital to establish the metrics used to evaluate the model's performance. There are various metrics, each serving a specific purpose. This project intends to introduce you to the most commonly used metrics for classification and regression tasks, show you how to calculate them, and provide guidance on interpreting them.
Training project
This project allows you to practice and strengthen your coding skills, helping you get ready for more advanced tasks ahead.
What you'll learn
Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:
Construct a confusion matrix with a model's list of correct classes and predictions.
Calculate the classifier's accuracy, precision, and recall using the confusion matrix.
Calculate other well-suited metrics for imbalanced data: balanced accuracy, F-score, and MCC.
Given the probabilities of positive class, construct a ROC curve. Use some basic geometry to calculate the AUC!
Calculate errors using true target variable values and those predicted by a simple regression model.
The coefficient of determination is crucial for regression tasks. It measures how well the model fits the data. Use the provided formula to calculate it!
Reviews
3 months ago
I learned a lot about evaluating machine learning models in this project. Going through confusion matrices, accuracy, precision, recall, AUC, and regression metrics like MSE, RMSE, MAE, and R² really helped me understand how to measure model performance. Working through the exercises step by step ma ...
2 years ago
Moderator
I have learned about various classification metrics, such as precision, recall, balanced accuracy, F1-score, and MCC. The MCC is a neat metric, liked it (bad it didn't emphasize on this one for a bit longer).
I also learned how to construct a ROC curve and calculate the area under it.
In addition, ...
Elizaveta Yurina
2 years ago
It's a roller coaster, some stages are incredibly hard to understand, some you can solve in 3 sec
4.3
Learners who completed this project within the Coding Machine Learning Algorithms course rated it as follows: