A binary classifier that predicts whether a tumour is malignant or benign — with feature importance analysis, confusion matrix, and cross-validation.
The Wisconsin Breast Cancer dataset is built into scikit-learn — 569 samples, 30 features, binary target.
from sklearn.datasets import load_breast_cancer
import pandas as pd
data = load_breast_cancer()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['target'] = data.target # 0 = malignant, 1 = benign
print(df.shape) # (569, 31)
print(df['target'].value_counts())
# 1 357 (benign)
# 0 212 (malignant)
print(df.describe().T[['mean','std','min','max']].head(5))Split the data, train the model, and check accuracy.
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import numpy as np
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
# Pipeline: scale + classify
pipeline = Pipeline([
('scaler', StandardScaler()),
('clf', RandomForestClassifier(n_estimators=100, random_state=42)),
])
pipeline.fit(X_train, y_train)
test_acc = pipeline.score(X_test, y_test)
print(f"Test accuracy: {test_acc:.4f}") # ~0.9737
# 5-fold cross-validation for a reliable estimate
cv_scores = cross_val_score(pipeline, X, y, cv=5, scoring='accuracy')
print(f"CV accuracy: {cv_scores.mean():.4f} +/- {cv_scores.std():.4f}")Test accuracy: 0.9737 CV accuracy: 0.9649 +/- 0.0167
See exactly which tumours the model gets wrong.
from sklearn.metrics import classification_report, ConfusionMatrixDisplay
import matplotlib.pyplot as plt
y_pred = pipeline.predict(X_test)
print(classification_report(y_test, y_pred, target_names=['Malignant','Benign']))
fig, ax = plt.subplots(figsize=(5,4))
ConfusionMatrixDisplay.from_predictions(
y_test, y_pred,
display_labels=['Malignant','Benign'],
cmap='Blues', ax=ax
)
plt.title('Breast Cancer Classifier — Confusion Matrix')
plt.tight_layout()
plt.savefig('confusion_matrix.png', dpi=120)
plt.show()precision recall f1-score support
Malignant 0.98 0.95 0.96 42
Benign 0.97 0.99 0.98 72
accuracy 0.97 114Find which measurements matter most for the prediction.
import pandas as pd
rf = pipeline.named_steps['clf']
importances = pd.Series(rf.feature_importances_, index=data.feature_names)
top10 = importances.nlargest(10)
plt.figure(figsize=(8,5))
top10.sort_values().plot(kind='barh', color='#1e3a5f')
plt.title('Top 10 Most Important Features')
plt.xlabel('Importance score')
plt.tight_layout()
plt.savefig('feature_importance.png', dpi=120)
plt.show()
print(top10)A 97% accurate cancer classifier in under 50 lines. The same Pipeline pattern — StandardScaler + Classifier — works for any tabular classification problem. Swap RandomForestClassifier for SVC, XGBClassifier, or LogisticRegression and compare accuracy.
Spotted a bug, broken code, or something that doesn't look right? Tell us what's off and we'll fix it.