Learn how Logistic Regression turns inputs into class probabilities for binary classification (e.g., Spam/Not-Spam), then evaluate it with a Confusion Matrix and a Classification Report.
Maps any real value to a probability in [0,1].
If P(y=1|x) ≥ 0.5 ⇒ class 1; else class 0 (threshold can be tuned).
Binary outcomes, linearly separable (or near-linear) in feature space. Fast, interpretable baseline.
# Step 1: Imports
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import classification_report, confusion_matrix
# Step 2: Data
data = load_breast_cancer()
X, y = data.data, data.target
# Step 3: Split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
# Step 4: Train
model = LogisticRegression(max_iter=10000)
model.fit(X_train, y_train)
# Step 5: Evaluate
y_pred = model.predict(X_test)
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
Tip: Use stratify=y to keep class balance in train/test splits.