Decision Trees and Random Forests
Rule-based and ensemble classifiers.
Decision Trees and Random Forests is a free Data Science Academy lesson on CoddyKit — lesson 3 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.
A Tree of Questions
A decision tree classifies by asking a chain of yes-or-no questions about your features until it reaches an answer. 🌳
Splits at Each Node
Every branch point is a split, a rule like is age above 30. The data flows left or right based on that test.
Leaves Hold the Answer
When no more useful questions remain, you hit a leaf. The class that dominates that leaf becomes the prediction.
Picking the Best Split
A tree chooses each split to make groups as pure as possible, measured by criteria like Gini impurity or entropy.
Easy to Read
One real strength is interpretability: you can follow the path of questions and explain exactly why a prediction was made.
Trees Love to Overfit
Left unchecked, a tree grows deep and memorizes the training data. That overfitting hurts accuracy on new, unseen rows.
Limit the Depth
You tame a tree by capping its growth with max_depth, trading a little training fit for far better generalization.
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(max_depth=4)Many Trees Beat One
A random forest trains many trees and lets them vote. The crowd is far steadier than any single deep tree.
Each Tree Sees Less
For variety, each tree trains on a random sample of rows and a random subset of features. That diversity is the secret sauce.
Build a Forest
It is one line in scikit-learn. Set n_estimators to choose how many trees join the ensemble.
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)Free Feature Importances
A trained forest tells you which inputs mattered most through feature_importances_, a quick guide to what drives predictions.
model.feature_importances_Quick Check
Let's confirm why a forest improves on a single tree.
Recap
A decision tree asks questions down to a leaf; a random forest votes across many trees to cut overfitting. 🎯
Frequently asked questions
Is the “Decision Trees and Random Forests” lesson free?
Yes — the full text of “Decision Trees and Random Forests” 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 “Decision Trees and Random Forests”?
Rule-based and ensemble classifiers. 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 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Decision Trees and Random Forests” 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