👉MICE is a powerful method for datasets with missing data across multiple variables. 

Let this slide guide you through how it works. 

#machinelearning #MICE #mlmodels #datascience #dataengineering #imputation #featureengineering

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

🤔 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 
https://www.trainindata.com/p/feature-engineering-for-machine-learning

#machinelearning #featurenegineering #dataimputation

In #ML, the accuracy of a classifier’s predictions is crucial. If your model's probabilities are off, probability calibration can correct that.✔️

Learn why calibration matters & how to do it in Python with scikit-learn 👇 https://www.blog.trainindata.com/probability-calibration-in-machine-learning/

Machine Learning is transforming insurance, but black-box models hurt trust and compliance. 🧐

Interpretability helps us:
✅ Spot biases
✅ Explain decisions
✅ Improve models

Understanding decisions = fairer, more transparent insurance. 💡

#MachineLearning #Insurance #AI