Machine Learning with Python

Data leakage is one of the main reasons why ML demos look impressive... and then fail in production. 📉 The model didn't become smarter. It just happened to see the correct answers in advance. In 4 minutes, you'll understand where data leaks hide. 🔍 Let's break it down below: 👇 1. Data Leakage 🕳️ Data leakage occurs when information that won't be available at the time of actual prediction is used during the model training process. Because of this, metrics on the validation stage can look much better than the actual quality of the model on new, previously unseen data. 2. Model Evaluation ⚖️ The test set isn't just "additional data". It's a simulation of the future. Only train the model on the information that would have been available to you at the time of prediction. Evaluate it on examples that the model couldn't have influenced during training. 3. Direct Leakage 🚨 This is the most obvious type of leakage. Examples: - a field with information from the future; - an ID that encodes the target variable; - a variable that appears only after an event has occurred; - duplicate records in both the training and test sets. If a feature doesn't exist at the time of inference (prediction), then it's likely a source of data leakage. 4. Indirect Leakage 🕵️ This is the type of leakage that most often traps teams. You perform normalization, imputation, feature selection, outlier removal, or dimensionality reduction before splitting the data into a training and test set. The model didn't directly see the data from the test set. But your preprocessing pipeline already saw it. 5. Train/Test Split ✂️ Wrong:

fit the scaler on all data → split the data → evaluate

Right:

split the data → fit the scaler only on the training set → apply it to both the training and test sets

The same idea applies to imputers, encoders, feature selection, PCA, and any preprocessing step that is trained on the data. 6. Cross-Validation 🔄 Each fold is a mini-experiment with a training and test set. Therefore, preprocessing should be performed within each fold. If you prepared the entire dataset once and then ran cross-validation, each fold would already have had access to its held-out data. 7. Pipelines 🛠️ A pipeline isn't just a way to make the code cleaner. It's also a defense against data leakage. Combine preprocessing, feature selection, and the model into a single pipeline, and then pass this pipeline to cross-validation or hyperparameter search (grid search). 8. AI Engineering Version 🤖 Data leaks also occur in RAG systems and when evaluating LLMs. Leakage occurs when you tune chunks, prompts, re-rankers, thresholds, or examples on the same evaluation dataset that you later present as "held-out". As a result, your benchmark turns into training data. 9. Leakage Checklist ✅ Before trusting the obtained metric, ask yourself: - Could this feature exist at the time of prediction? - Was any transformation (transform) step trained (fit) on the test data? - Did cross-validation include the entire pipeline? - Were we tuning parameters on the final evaluation dataset? If the answer is "yes", then the metric likely doesn't reflect the actual quality of the model. #MachineLearning #DataScience #MLOps #DataLeakage #ArtificialIntelligence #TechTips ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Stop discovering ML Python libraries one random tutorial at a time 🛑 Best-of Machine Learning with Python is a curated GitHub index of open-source machine learning Python libraries for builders who need a faster way to compare the ecosystem 📚. It helps you shortlist tools by grouping projects into categories and ranking them with a project-quality score based on metrics collected from GitHub and package managers 📊. Key features: • 920-project index – a large scan-friendly map of open-source ML Python projects 🗺️ • 34 categories – browse by area like ML frameworks, NLP, image data, AutoML, deployment, interpretability, and more 🧩 • Quality-score ranking – projects are ordered using an automated score from repo and package-manager signals ⚙️ • Rich project metadata – entries show signals like stars, forks, issues, contributors, activity, downloads, and dependencies 📈 • Weekly updates + contributions – the list is updated regularly and can be improved via issues, PRs, or projects.yaml edits 🔄 It’s open-source (CC BY-SA 4.0 license) 📜. https://github.com/lukasmasuch/best-of-ml-python 🔗 #MachineLearning #Python #ML #OpenSource #DataScience #TechStack ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚 This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊 It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖 Free public repository on GitHub. 💻✨ https://github.com/dair-ai/Mathematics-for-ML #MachineLearning #Mathematics #DataScience #Learning #GitHub #AI

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Pandas vs Polars vs DuckDB: Which Library Should You Choose? 🤔📊 pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows 📝📈. Polars focus on fast, memory-efficient DataFrame processing ⚡💾, while DuckDB brings a SQL-first approach for querying local files and embedded analytics 🗄️🔍. Each tool fits a different kind of local data workflow 🛠️. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases 🏆🔗. More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ 🔗 #DataScience #Pandas #Polars #DuckDB #Python #Analytics

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