Helping people live longer, healthier lives. Views my own. ❤️Serve one another in LOVE❤️
Melbourne
Joined February 2011
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Day 1 of my one-food-a-day reading challenge: soy 🫛
In Eat to Beat Disease, Dr. William Li links soy foods with lower risks of breast cancer, prostate adenocarcinoma and coronary heart disease.
Edamame, tofu, soy milk, miso, natto… one bean, so many forms!
🤖 Made with AI
Reading Dr. William Li’s Eat to Beat Disease has me excited about food 😂 So I’m setting myself a little challenge: share one food from the book each day, with a short note on what he says about it. Starting here —with a furry witness 🌱🐾
Your LDL is creeping up again — does that mean yet more foods to avoid?
What if there are everyday foods you can eat that help lower bad cholesterol?
In this article, I looked at three of them, backed by rigorous studies, with simple, practical ways to use them in your kitchen.
Link to the article: medium.com/health-science/th…
Did you know 50% of people who die of a heart attack have no prior symptoms? 🫀
Here are my 3 key takeaways from @HillaryLinMD’s video “The Heart Attack You Won’t See Coming,” a must-watch for anyone focused on cardiovascular prevention. 👇
2️⃣ ApoB can reveal what LDL-C misses.
It reflects the number of atherogenic particles in your blood. These particles can enter and become trapped in artery walls, driving plaque buildup.
3️⃣ Build a margin of safety for prevention.
A “normal” or green result on your lab report doesn’t mean zero risk. Just as engineers build a margin of safety into a bridge to withstand an earthquake, think of lowering ApoB further as creating a buffer for your arteries: less “fuel” for plaque when stress, poor sleep, or inflammation add strain.
Work with your doctor to set personal targets early based on your overall risk. ❤️
📺 Full video: youtu.be/fX33g1zFZSo
What could you do to lower your risk of a heart attack?
I turned Australia’s heart-health guideline into this simple guide. ❤️
Preparing for the Databricks Data Engineer Associate exam?
I published a prep test on @udemy that contains 100+ practice questions covering all domains in the latest exam guide.
Reply “DE” or DM me for a free course voucher.
Dr Mena Ning WANG, PhD retweeted
🌟 Editor's Pick 🌟 @mena_wang shares key takeaways and actionable steps for business decision makers.
towardsdatascience.com/when-…
Dr Mena Ning WANG, PhD retweeted
When do you have "enough data" to make a confident decision? 📊
@mena_wang dives into how statistical power and sample size help you confidently separate signal from noise, even when data is messy.
towardsdatascience.com/when-…
Dr Mena Ning WANG, PhD retweeted
Did you know that very different business realities can hide behind the same bar chart — one of the most common ways insights are presented in the business world?
@mena_wang shares practical steps to analyze group differences and avoid costly misinterpretations.
towardsdatascience.com/when-…
Dr Mena Ning WANG, PhD retweeted
Struggling with model performance? Learn how to identify and manage the problematic data points, not just your data. @mena_wang unveils a Cook's Distance-inspired approach to pinpoint these influential observations.
towardsdatascience.com/help-…
Dr Mena Ning WANG, PhD retweeted
That 5-point difference in your dashboard might be meaningless. @mena_wang's new article reveals how a simple bar chart can hide three distinct business realities and lead to misinterpretations.
towardsdatascience.com/when-…
Dr Mena Ning WANG, PhD retweeted
Build more robust and reliable ML models. @mena_wang's guide to identifying influential observations teaches you to move beyond simple outlier detection, equipping you with a diagnostic tool for your workflow.
towardsdatascience.com/help-…
With this helper function, you can easily detect and diagnose the most influential points in the training set that pull the model’s predictions away from the “general pattern” reflected in the rest of the data.
Learn more on TDS: towardsdatascience.com/help-…
Worried your model is being held hostage by a few bad apples? Learn how to identify and neutralize influential data points that cripple generalization. @mena_wang unveils a smarter approach to anomaly detection.
towardsdatascience.com/help-…
Would you like to have a comprehensive evaluation report built for your ML model in just one line? 😎
This is what the `evaluation` method in `MLarena` produces for any sklean-style classification estimators. 😍
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#MachineLearning #DataScience #pythonlearning #MLOps
ALT This is the screenshot of the output from mlarena's `evaluation` method for classification models. It includes key metrics such as AUC, MCC, precision, recall, F1, and F-beta (when beta is specified). The visual outputs feature a ROC-AUC curve (bottom left), a confusion matrix (bottom right), and a precision–recall–threshold plot at the top. In this top plot, precision (blue), recall (red), and F-beta (green, with β = 1 by default) are shown across different classification thresholds, with a vertical dotted line indicating the current threshold to highlight the trade-off. These visualizations are useful not only for technical diagnostics, but also for supporting discussions around threshold selection with domain experts (more on threshold optimization later).
This is an overview of the metrics and plots offered in evaluation reports for classification and regression models, respectively.
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ALT an overview of MLarena’s evaluation reports for both classification and regression tasks
Learn more about MLarena 📦, an open source Python package for streamlining your ML workflow in this article published on @TDataScience
towardsdatascience.com/build…
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