In #ML, the accuracy of a classifier’s predictions is crucial. If your model's probabilities are off, probability calibration can correct that.✔️ Check out this blog to learn why calibration matters and how to implement it in Python using scikit-learn👉blog.trainindata.com/probability-ca…

🤔 Have you used missing category imputation in your projects? Check out this reel 👇 💡 Want to dive deeper into feature engineering and data imputation? Check out our course  trainindata.com/p/feature-engi… #machinelearning #featurenegineering #dataimputation

Discover the truth behind SMOTE for imbalanced data and explore better alternatives. Learn more about metrics, threshold optimization, and classifier calibration in this video. If you find it useful, don’t hesitate to share with your peers! 🙏 youtube.com/watch?v=blcOOh… #ml

The most crucial component of any machine learning project is data!    ▶️ 90% of the time is spent on data preprocessing   ▶️ 10% of the time is spent on model building, tuning and evaluation. #machinelearning #ML #MLmodels #preprocessing #modelbuilding #datascience

Next Monday on Data Bites : Probe Feature Selection Want to know more? Click the link below to subscribe and stay tuned!👇 f.mtr.cool/xefqrzzgeh #machinelearning #datascience #imbalanceddata #undersampling #mlmodels #ML

🚨 New Course Now Live! Clustering & Dimensionality Reduction at Train in Data Learn to apply unsupervised ML in practice 👇 ✅ K-Means, DBSCAN, HDBSCAN, Graph-based ✅ PCA & UMAP ✅ Real-world projects incl. RNA case study Find out more👇 f.mtr.cool/gapkzijkte

Most commonly used encoding techniques ⬇️ 1. OneHotEncoder 2. OrdinalEncoder 3. TargetEncoder When one-hot encoding gets too complex and ordinal encoding leads to inaccuracies, TargetEncoding often becomes the best choice. Learn more at the link below. #targetencoder #ML

🐍Python libraries that implement agnostic global explainability methods 👇  #python #machinelearning #MLModel #datascience #dataengineering

In this video, I review hyperparameter optimization techniques like Grid Search, Random Search, & Bayesian methods. Learn their pros, cons, and best applications for both low and high-dimensional spaces!  What techniques do you use?  📽️f.mtr.cool/qrakmzochy

Next Monday on Data Bites : Everybody says “SMOTE does not work”. Want to know more? Click the link below to subscribe and stay tuned!👇 f.mtr.cool/pinchbaedf #machinelearning #datascience #smote #mlmodels #ML

🚨 Just launched: our new course on Clustering & Dimensionality Reduction is live at Train in Data! Learn to group data, reduce complexity with PCA & UMAP, and tackle real-world projects (no experience needed!) 🎓 Join us: f.mtr.cool/ivfpdpztru #machinelearning #clustering

🚨 SMOTE has long been hailed as the go-to solution for imbalanced datasets, but it only works in specific scenarios.  In this article, we explore when SMOTE is truly effective and why it’s remained popular.  Check it out! f.mtr.cool/ahzkjrwqoq f.mtr.cool/dyynjqxahg

In case you were wondering 👇  #machinelearning #ai #datascience #dataengineering #mlmodels

Deep learning has transformed our daily lives, but designing neural networks remains a challenge.  Automated hyperparameter optimization (HPO) streamlines the process. This paper reviews key techniques & tools for improving model accuracy & efficiency. 📃f.mtr.cool/wowjcrmwjg

Next Monday on Data Bites : How to Write a Winning Data Science CV Want to know more? Click the link below to subscribe and stay tuned!👇 f.mtr.cool/nozrfuruar #machinelearning #datascience #CV #mlmodels #ML #MLCareer #MLresume

🚨 It’s here! Our new course on Clustering & Dimensionality Reduction just dropped 🎉 Learn how to group data (K-Means, DBSCAN, Louvain) + simplify it with PCA & UMAP, no prior experience needed! Hands-on & practical 👇 👉  f.mtr.cool/zshxexbrds #MachineLearning #DataScience

Can we use statistical tests to select features? 🤔 Turns out, we can! 🎉 In the slides below, we’ll explore the most commonly used statistical tests for feature selection, along with their advantages and limitations. 👇 #machinelearning #datascience #featureselection

ELI5 now supports scikit-learn 1.6.0! 🎉It wasn’t working with the latest version of scikit-learn, but that’s a thing of the past. As of now, ELI5 has released a new version with full support for scikit-learn >1.6.0 and Python >3.10. Check it out 👇 f.mtr.cool/shvdcflpqa

Model performance matters! 🎯  In this article, we break down essential evaluation metrics for classification models, starting with the Confusion Matrix. Perfect for anyone looking to build reliable #machinelearning systems! Have a good read👇 f.mtr.cool/oyugswlyaj

Next Monday on Data Bites : Working with imbalanced data? Follow these 3 steps. Want to know more? Click the link below to subscribe and stay tuned!👇 f.mtr.cool/svpfklfpda #machinelearning #datascience #CV #mlmodels #ML #MLCareer #MLresume