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    "# Chapter Exercise\n"
   ]
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  {
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   "source": [
    "Apply the key ideas from this chapter in the connected task below. Work in your own notebook or local project. Keep your implementation, reasoning, and verification results together.\n"
   ]
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    "## Evaluate a classifier for a decision\n",
    "\n",
    "1. Reuse the chapter's real shopping-session data and define the target, features, baseline, and training/evaluation separation.\n",
    "\n",
    "2. Compare scaled nearest neighbors with logistic regression using preprocessing fitted only on training data. Choose k or a decision threshold using the chapter's validation approach.\n",
    "\n",
    "3. Report a confusion matrix, accuracy, precision, recall, and an explicit error-cost comparison on held-out data. Explain which classifier you would use and why the highest accuracy need not give the best decision.\n"
   ]
  },
  {
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   "id": "fbaf101f",
   "metadata": {},
   "source": [
    "Return to the chapter lessons as needed: [Classification](1401-classification.ipynb), [Nearest Neighbors](1402-nearest-neighbors.ipynb), [Training, Testing, and Accuracy](1403-training-testing-accuracy.ipynb), [Nearest Neighbors with scikit-learn](1404-knn-scikit-learn.ipynb), [Evaluating Classifiers](1405-evaluating-classifiers.ipynb), [Logistic Regression](1406-logistic-regression.ipynb).\n"
   ]
  }
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