## Breast Cancer Dataset Overview

The **breast_cancer** dataset from scikit-learn is a widely used binary classification dataset for predicting whether a tumor is malignant or benign based on various features computed from digitized images of fine needle aspirate (FNA) of breast masses.

Mean of distances from center to points on the perimeter

Mean of local variation in radius lengths

Mean severity of concave portions of the contour

Mean number of concave portions of the contour

"Worst" or largest mean value for texture

"Worst" or largest mean value for perimeter

"Worst" or largest mean value for smoothness

"Worst" or largest mean value for compactness

"Worst" or largest mean value for concavity

"Worst" or largest mean value for concave points

"Worst" or largest mean value for symmetry

"Worst" or largest mean value for fractal dimension

### Data Overview

- Number of samples: 569;
- Number of features: 30;
- Target variable: `target` (0 = malignant, 1 = benign);
- Task: Predict whether a tumor is malignant or benign based on the features above.


import unittest
import importlib
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (
    accuracy_score, precision_score, recall_score, f1_score,
    roc_auc_score, confusion_matrix
)

def _dynamic_test(test_case, condition, success_msg, failure_msg):
    if condition:
        test_case._testMethodName = success_msg
        test_case.assertTrue(True, success_msg)
    else:
        test_case._testMethodName = failure_msg
        test_case.fail(failure_msg)

class TestClassificationMetricsExtended(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.user = importlib.import_module("user_code")

        data = load_breast_cancer()
        X_train, X_test, y_train, y_test = train_test_split(
            data.data, data.target, test_size=0.3, random_state=42
        )

        model = LogisticRegression(max_iter=1000)
        model.fit(X_train, y_train)
        y_pred = model.predict(X_test)
        y_prob = model.predict_proba(X_test)[:, 1]

        cls.true_metrics = {
            "accuracy": accuracy_score(y_test, y_pred),
            "precision": precision_score(y_test, y_pred),
            "recall": recall_score(y_test, y_pred),
            "f1": f1_score(y_test, y_pred),
            "roc_auc": roc_auc_score(y_test, y_prob),
            "cm": confusion_matrix(y_test, y_pred),
            "mean_cv": cross_val_score(model, data.data, data.target, cv=5).mean(),
        }

    def test_accuracy(self):
        cond = abs(self.user.accuracy - self.true_metrics["accuracy"]) < 1e-6
        _dynamic_test(self, cond, "Accuracy computed correctly.", "Accuracy is incorrect.")

    def test_precision(self):
        cond = abs(self.user.precision - self.true_metrics["precision"]) < 1e-6
        _dynamic_test(self, cond, "Precision computed correctly.", "Precision is incorrect.")

    def test_recall(self):
        cond = abs(self.user.recall - self.true_metrics["recall"]) < 1e-6
        _dynamic_test(self, cond, "Recall computed correctly.", "Recall is incorrect.")

    def test_f1(self):
        cond = abs(self.user.f1 - self.true_metrics["f1"]) < 1e-6
        _dynamic_test(self, cond, "F1 score computed correctly.", "F1 score is incorrect.")

    def test_roc_auc(self):
        cond = abs(self.user.roc_auc - self.true_metrics["roc_auc"]) < 1e-6
        _dynamic_test(self, cond, "ROC–AUC computed correctly.", "ROC–AUC is incorrect.")

    def test_confusion_matrix(self):
        cond = np.array_equal(self.user.cm, self.true_metrics["cm"])
        _dynamic_test(self, cond, "Confusion matrix computed correctly.", "Confusion matrix is incorrect.")

    def test_cross_validation(self):
        cond = abs(self.user.mean_cv - self.true_metrics["mean_cv"]) < 1e-6
        _dynamic_test(self, cond, "Cross-validation mean accuracy computed correctly.", "Cross-validation mean accuracy is incorrect.")

if __name__ == "__main__":
    unittest.main(argv=["first-arg-is-ignored"], exit=False)

test_main.py

A comprehensive course for intermediate learners to master the key evaluation metrics used in machine learning, covering both supervised and unsupervised tasks. The course progresses from foundational concepts to practical Python implementations, interpretation of results, and a comparative overview of metric types and their trade-offs.

Explore the fundamental metrics used to evaluate classification models, including their definitions, formulas, Python implementations, and interpretation. Includes advanced metrics and model validation techniques relevant to classification.

Delve into the core metrics for evaluating regression models, including their mathematical foundations, Python implementations, and result interpretation. Includes advanced validation techniques relevant to regression.

Examine the key metrics for evaluating unsupervised learning models, including clustering, dimensionality reduction, and anomaly detection. Learn their mathematical foundations, Python implementations, and interpretation.

Challenge: Classification Metrics

Breast Cancer Dataset Overview

Data Overview

Challenge: Classification Metrics

Breast Cancer Dataset Overview

Data Overview

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