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   "cell_type": "markdown",
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   "source": [
    "# Digitial Classification\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n",
      "KNN score: 0.953661\n",
      "LogisticRegression score: 0.908248\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "from sklearn import datasets, neighbors, linear_model\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data\n",
    "y_digits = digits.target\n",
    "\n",
    "n_samples = len(X_digits)\n",
    "n_train = int(0.4 * n_samples)\n",
    "\n",
    "X_train = X_digits[:n_train]\n",
    "y_train = y_digits[:n_train]\n",
    "X_test = X_digits[n_train:]\n",
    "y_test = y_digits[n_train:]\n",
    "\n",
    "knn = neighbors.KNeighborsClassifier()\n",
    "logistic = linear_model.LogisticRegression()\n",
    "\n",
    "print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test))\n",
    "print('LogisticRegression score: %f' % logistic.fit(X_train, y_train).score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "* [Supervised learning: predicting an output variable from high-dimensional observations](http://scikit-learn.org/stable/tutorial/statistical_inference/supervised_learning.html)\n",
    "* [Digits Classification Exercise](http://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html)\n"
   ]
  }
 ],
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