Logistic Regression for Yes/No
Probabilities and decision thresholds.
Logistic Regression for Yes/No is a free Data Science Academy lesson on CoddyKit — lesson 1 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.
Classes, Not Numbers
Classification predicts a category, not a number. Will this email be spam or not? That yes-or-no answer is what we are after. ✉️
The Yes/No Workhorse
Logistic regression is the classic starting point for two-class problems. It is simple, fast, and surprisingly hard to beat.
It Outputs Probability
Despite its name, logistic regression returns a probability between 0 and 1, like a 0.82 chance this customer will churn.
The Sigmoid Squeeze
A curve called the sigmoid squeezes any value into the 0-to-1 range. That is how raw scores become clean probabilities.
Import and Create
You build it just like any estimator: import the class, then create the model object ready to learn.
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()Train It With fit
Show it your features and labels with fit, and it learns the weights that separate the two classes.
model.fit(X_train, y_train)Predict the Label
Call predict to get the chosen class for each row, returning a clean 0 or 1 for every example.
labels = model.predict(X_test)Ask for the Probability
Want the raw confidence instead? predict_proba gives the probability of each class, so you see how sure the model is.
probs = model.predict_proba(X_test)The Decision Threshold
To turn a probability into a label, you compare it to a threshold, usually 0.5. Above it means yes, below it means no.
Move the Threshold
You can raise or lower that threshold on purpose. A stricter cutoff catches fewer cases but trusts each one more.
Read the Coefficients
Each feature gets a coefficient. A positive one pushes the prediction toward yes, a negative one toward no.
model.coef_Quick Check
Let's confirm what logistic regression actually hands back.
Recap
Logistic regression turns features into a probability, then a threshold decides yes or no. Simple, fast, and a great first classifier. 🎯
Frequently asked questions
Is the “Logistic Regression for Yes/No” lesson free?
Yes — the full text of “Logistic Regression for Yes/No” 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 “Logistic Regression for Yes/No”?
Probabilities and decision thresholds. 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 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Logistic Regression for Yes/No” 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
- Logistic Regression for Yes/No
- k-Nearest Neighbors
- Decision Trees and Random Forests
- Gradient Boosting Essentials