Ridge and Lasso Regularization
Taming overfitting with penalties.
Ridge and Lasso Regularization is a free Data Science Academy lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Data Science Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
When a Line Overfits
Plain linear regression can chase noise, growing huge coefficients that fit training data yet fail on new data. That trap is called overfitting. 😬
Add a Penalty
Regularization fixes this by adding a penalty for large coefficients. The model now balances fitting the data against keeping weights small.
Ridge Shrinks Weights
Ridge penalizes the squared size of the coefficients. It pulls every weight toward zero but rarely makes any exactly zero.
from sklearn.linear_model import Ridge
model = Ridge(alpha=1.0).fit(X, y)Lasso Can Zero Them Out
Lasso penalizes the absolute size instead. This can drive weak coefficients all the way to zero, dropping those features entirely.
from sklearn.linear_model import Lasso
model = Lasso(alpha=0.1).fit(X, y)Lasso Selects Features
Because Lasso zeroes weak weights, it doubles as feature selection. The surviving non-zero coefficients are the ones it found useful.
Alpha Sets the Strength
The alpha knob controls how hard the penalty pushes. Bigger alpha means stronger shrinkage and a simpler, smoother model.
Too Much Alpha Underfits
Crank alpha too high and coefficients shrink so far the model ignores real signal. That opposite mistake is called underfitting.
Scale Before You Penalize
Penalties compare coefficients directly, so features must share a scale first. Standardize your data or the penalty hits big-unit features unfairly.
from sklearn.preprocessing import StandardScalerRidge or Lasso?
Use Ridge when most features help a little, and Lasso when you suspect many are useless and want a sparse, lean model.
ElasticNet Blends Both
Can't decide? ElasticNet mixes the Ridge and Lasso penalties, giving you shrinkage and some feature dropping at the same time.
from sklearn.linear_model import ElasticNetTune With Cross-Validation
Never guess alpha by hand. Try a range and pick the value that scores best on held-out folds with cross-validation.
from sklearn.linear_model import RidgeCVQuick Check
One key difference sets these two apart in practice.
Recap
Regularization tames overfitting: Ridge shrinks weights, Lasso can zero them, and alpha sets the strength. Scale first, then tune. 🎯
Frequently asked questions
Is the “Ridge and Lasso Regularization” lesson free?
Yes — the full text of “Ridge and Lasso Regularization” is free to read here on the web, and the Data Science Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Data Science Academy course, upgrade to CoddyKit PRO.
What will I learn in “Ridge and Lasso Regularization”?
Taming overfitting with penalties. You practise Data Science Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Data Science Academy?
No prior experience is required. Data Science Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Ridge and Lasso Regularization” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Data Science Academy lesson?
Yes. Every Data Science Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Linear Regression Revisited
- Ridge and Lasso Regularization
- Decision Tree Regression
- Random Forest for Regression