Evals in Data Science
🔥 Building models is fun… but here’s the real test: is your model actually any good, or just pretending? 👀
Evaluations, or evals, are our model’s report card. They tell us:
- For a spam filter: Do we catch all spam (recall) without misclassifying grandma’s emails as junk (precision)?
- For price prediction: How close are our predictions on average (RMSE)?
But evals aren’t just about numbers - they influence trust, fairness, and real-world usefulness of our models.
Discussion prompts:
- What’s your go-to evaluation metric and why?
- Seen a model that looked great on paper but flopped in reality?
- Should fairness & usability be considered first-class evaluation metrics alongside accuracy?
Free book to dive deeper:
- Fairness and Machine Learning: rigorous, practical guide to evaluating models for fairness: https://fairmlbook.org/
Drop your thoughts below ⬇️
🔥 Building models is fun… but here’s the real test: is your model actually any good, or just pretending? 👀
Evaluations, or evals, are our model’s report card. They tell us:
- For a spam filter: Do we catch all spam (recall) without misclassifying grandma’s emails as junk (precision)?
- For price prediction: How close are our predictions on average (RMSE)?
But evals aren’t just about numbers - they influence trust, fairness, and real-world usefulness of our models.
Discussion prompts:
- What’s your go-to evaluation metric and why?
- Seen a model that looked great on paper but flopped in reality?
- Should fairness & usability be considered first-class evaluation metrics alongside accuracy?
Free book to dive deeper:
- Fairness and Machine Learning: rigorous, practical guide to evaluating models for fairness: https://fairmlbook.org/
Drop your thoughts below ⬇️
❤4
AI vs ML vs Deep Learning 🤖
You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not.
AI (Artificial Intelligence): the big umbrella. Anything that makes machines “smart.” Could be rules, could be learning.
ML (Machine Learning): a subset of AI. Machines learn patterns from data instead of being explicitly programmed.
Deep Learning: a subset of ML. Uses neural networks with many layers (deep) powering things like ChatGPT, image recognition, etc.
Think of it this way:
AI = Science
ML = A chapter in the science
Deep Learning = A paragraph in that chapter.
You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not.
AI (Artificial Intelligence): the big umbrella. Anything that makes machines “smart.” Could be rules, could be learning.
ML (Machine Learning): a subset of AI. Machines learn patterns from data instead of being explicitly programmed.
Deep Learning: a subset of ML. Uses neural networks with many layers (deep) powering things like ChatGPT, image recognition, etc.
Think of it this way:
AI = Science
ML = A chapter in the science
Deep Learning = A paragraph in that chapter.
❤9👏3
Overfitting vs Underfitting 🎯
Why do ML models fail? Usually because of one of these two villains:
Overfitting: The model memorizes training data but fails on new data. (Like a student who memorizes past exam questions but can’t handle a new one.)
Underfitting: The model is too simple to capture patterns. (Like using a straight line to fit a curve.)
The sweet spot? A model that generalizes well.
Note: Regularization, cross-validation, and more data usually help fight these problems.
Why do ML models fail? Usually because of one of these two villains:
Overfitting: The model memorizes training data but fails on new data. (Like a student who memorizes past exam questions but can’t handle a new one.)
Underfitting: The model is too simple to capture patterns. (Like using a straight line to fit a curve.)
The sweet spot? A model that generalizes well.
Note: Regularization, cross-validation, and more data usually help fight these problems.
❤12
The Curse of Dimensionality 🧩
Here’s something that trips up many beginners:
More features ≠ always better.
When your dataset has too many features (dimensions), weird things happen:
⛔️ Distances between points become meaningless.
⛔️ Models struggle to generalize.
⛔️Training time explodes.
👉 Solution: techniques like PCA, feature selection, or just collecting smarter data instead of more data.
Remember: Adding noise isn’t adding information.
Here’s something that trips up many beginners:
More features ≠ always better.
When your dataset has too many features (dimensions), weird things happen:
⛔️ Distances between points become meaningless.
⛔️ Models struggle to generalize.
⛔️Training time explodes.
👉 Solution: techniques like PCA, feature selection, or just collecting smarter data instead of more data.
Remember: Adding noise isn’t adding information.
❤3
🚀 Fast-Track Machine Learning Roadmap 2025
Mindset: Build first, learn just-in-time. Share progress publicly (GitHub + posts). Consistency > cramming.
Weeks 1–2: Master Python, NumPy, Pandas, EDA, and data cleaning. Mini-win: load CSVs, handle missing data.
Weeks 3–6: Learn ML fundamentals with scikit-learn — train/test splits, cross-validation, classifiers (LogReg, RF, XGB), and regressors. Project: spam classifier or house price predictor.
Weeks 7–10: Dive into deep learning — tensors, autograd, PyTorch. Build CNN or text classifier + track experiments (Weights & Biases).
Weeks 11–12: Specialize (NLP, CV, recommenders, MLOps) and ship a niche AI app.
————————
Weekly Routine:
Mon-Tue: Learn concept + code example
Wed-Thu: Build feature + log metrics
Fri: Refactor + README + demo
Sat: Share + get feedback + plan fixes
Sun: Rest & review
————————
Portfolio Tips: Clear READMEs, reproducible env, demo videos, honest metric analysis. Avoid “math purgatory” and messy repos. Ship small every week!
————————
This approach gets you practical, portfolio-ready ML skills in ~3-4 months with real projects and solid evaluation for 2025 job markets!
Mindset: Build first, learn just-in-time. Share progress publicly (GitHub + posts). Consistency > cramming.
Weeks 1–2: Master Python, NumPy, Pandas, EDA, and data cleaning. Mini-win: load CSVs, handle missing data.
Weeks 3–6: Learn ML fundamentals with scikit-learn — train/test splits, cross-validation, classifiers (LogReg, RF, XGB), and regressors. Project: spam classifier or house price predictor.
Weeks 7–10: Dive into deep learning — tensors, autograd, PyTorch. Build CNN or text classifier + track experiments (Weights & Biases).
Weeks 11–12: Specialize (NLP, CV, recommenders, MLOps) and ship a niche AI app.
————————
Weekly Routine:
Mon-Tue: Learn concept + code example
Wed-Thu: Build feature + log metrics
Fri: Refactor + README + demo
Sat: Share + get feedback + plan fixes
Sun: Rest & review
————————
Portfolio Tips: Clear READMEs, reproducible env, demo videos, honest metric analysis. Avoid “math purgatory” and messy repos. Ship small every week!
————————
This approach gets you practical, portfolio-ready ML skills in ~3-4 months with real projects and solid evaluation for 2025 job markets!
❤10
📚 Data Science Riddle
You have a dataset with 1,000 samples and 10,000 features. What’s a common problem you might face when training a model on this data?
You have a dataset with 1,000 samples and 10,000 features. What’s a common problem you might face when training a model on this data?
Anonymous Quiz
23%
Underfitting
57%
Overfitting due to high dimensionality
6%
Data leakage
14%
Incorrect feature scaling
❤3👍1😁1