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    "# Using LogisticRegression in a Pipeline\n",
    "\n",
    "This notebook shows how to use `LogisticRegression` in a scikit-learn `Pipeline` with preprocessing, such as `StandardScaler`, to handle binary classification.\n",
    "\n",
    "## Setup\n",
    "We use a synthetic dataset for reproducibility."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-22T14:42:06.197077Z",
     "start_time": "2025-07-22T14:42:05.118559Z"
    }
   },
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from glmpynet import LogisticRegression\n",
    "\n",
    "# Generate synthetic dataset\n",
    "X, y = make_classification(n_samples=200, n_features=20, n_classes=2, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create and fit pipeline\n",
    "pipeline = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('logistic_net', LogisticRegression())\n",
    "])\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Predict and evaluate\n",
    "y_pred = pipeline.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Pipeline Accuracy: {accuracy:.2f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline Accuracy: 0.88\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explanation\n",
    "- The pipeline combines `StandardScaler` for feature scaling and `LogisticRegression` for classification.\n",
    "- The dataset is the same as in the basic example, ensuring consistency.\n",
    "- Accuracy is similar to the basic example but may improve slightly due to scaling.\n",
    "- With `glmnet`, expect comparable integration but potentially better performance on high-dimensional data."
   ]
  }
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