imbalanced-learn VS scikit-learn

There's a pletora of undersampling and oversampling models you can try out. To avoid removing information form the dataset, you can focus on oversampling techniques. You can try imbalanced-learn or smote-variants. Given enough data, using fully synthetic data is also an option, you can check ydata-synthetic for it. Let us know how it turned out!

Another conspicuous thing is the lack of vibe-coded PRs on mature open source projects. Maybe it's because these projects have erected policies limiting AI contributions, but given the high scores on SWEBench, you'd expect _something_ to come of it?

And yet in real world use you get stuff like https://github.com/scikit-learn/scikit-learn/pull/32101

Start Simple, Build Confidence Project: Scikit-learn After the intense first experience with BEHAVIOR-1K, I needed something more approachable. I went straight to Scikit-learn's good first issue label and found a task that seemed manageable: changing relative imports to absolute imports in Cython files. From this

The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A…

For apps demanding robust machine learning capabilities, frameworks like TensorFlow provide the scalability and flexibility needed to handle large-scale data and models. These tools are essential for developers building features like recommendation engines or predictive analytics.

Machine learning (ML) teaches computers to learn from data, like predicting user clicks. Start with simple models like regression (predicting numbers) and clustering (grouping data). Deep learning uses neural networks for complex tasks, like image recognition in a Vue.js gallery. Tools like Scikit-learn and PyTorch make it easier.

Scikit-learn Documentation: https://scikit-learn.org/

7. Scikit-learn - Machine Learning

Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python, try projects that combine data with everyday problems. For example, build a simple recommendation system using Pandas and scikit-learn.

scikit-learn (optional): Useful for additional training or evaluation tasks.

The race condition bugs are typically hidden by different software layers. For instance, we found one that involves OpenBLAS's pthreads-based thread pool management and maybe its scipy bindings:

- https://github.com/scipy/scipy/issues/21479

it might be the same as this one that further involves OpenMP code generated by Cython:

- https://github.com/scikit-learn/scikit-learn/issues/30151

We haven't managed to write minimal reproducers for either of those but as you can observe, those race conditions can only be triggered when composing many independently developed components.