Day 24 of 30Artificial Intelligence
🌸Your First ML Model — Classification
🎯 Today's objective
Train a classifier with scikit-learn.
📖 Key concepts
- Classification predicts a category
- fit() learns, predict() guesses
- train_test_split
- accuracy_score evaluates
💻 Try this code
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
X, y = load_iris(return_X_y=True)
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2)
m = KNeighborsClassifier().fit(Xtr, ytr)
print("accuracy:", accuracy_score(yte, m.predict(Xte)))👥 Learn it your way
🧒 Young Explorers
You just trained a real AI that tells flower species apart — in ~8 lines!
Your task
Run the iris example; change test_size and watch accuracy move.
💼 Professionals
Understand the fit/predict API, baseline accuracy, and why KNN is a sane first try.
Your task
Swap KNN for DecisionTreeClassifier; compare accuracy and comment.
🤖 AI connection
This fit→predict→evaluate loop is identical whether you classify flowers, spam, or tumours — it scales straight up to industry ML.
✅ Quick check
What does .fit() do?
Trains the model on the training data.
What does classification predict?
A category/class label.