{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PYT-DS SAISOFT\n",
    "\n",
    "* [Overview 1](https://github.com/4dsolutions/Python5/blob/master/OverviewNotes_PYTDS.ipynb)\n",
    "* [Overview 2](https://github.com/4dsolutions/Python5/blob/master/OverviewNotes_PYTDS_2.ipynb)\n",
    "\n",
    "<a data-flickr-embed=\"true\"  href=\"https://www.flickr.com/photos/kirbyurner/27963484878/in/album-72157693427665102/\" title=\"Barry at Large\"><img src=\"https://farm1.staticflickr.com/969/27963484878_b38f0db42a_m.jpg\" width=\"240\" height=\"180\" alt=\"Barry at Large\"></a><script async src=\"//embedr.flickr.com/assets/client-code.js\" charset=\"utf-8\"></script>\n",
    "\n",
    "# DATA SCIENCE WITH PYTHON\n",
    "\n",
    "\n",
    "[Click here](http://nbviewer.jupyter.org/github/4dsolutions/Python5/blob/master/OverviewNotes_PYTDS_3.ipynb) to view this JN on nbviewer.\n",
    "\n",
    "In the concluding sessions of this course, I have shifted from talking about the data pipeline, to the functions at the end of the tunnel, our Machine Learning algorithms, which I've also likened to a stable of horses, in terms of how we \"race\" them to find the best.  Choosing the best horse for your application takes experience.  Don't expect to become a data scientist overnight.\n",
    "\n",
    "In our sequence below, I start with a famous, oft used dataset, made of 28 by 28 numpy arrays, representing grayscale images of the numerals 0 through 9, quite a few specimens of each.  They're labeled rows.  We know the digits.  Lets take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "print(digits.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optical Recognition of Handwritten Digits Data Set\n",
      "===================================================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 5620\n",
      "    :Number of Attributes: 64\n",
      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      "    :Missing Attribute Values: None\n",
      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      "    :Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
      "\n",
      "The data set contains images of hand-written digits: 10 classes where\n",
      "each class refers to a digit.\n",
      "\n",
      "Preprocessing programs made available by NIST were used to extract\n",
      "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
      "total of 43 people, 30 contributed to the training set and different 13\n",
      "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
      "4x4 and the number of on pixels are counted in each block. This generates\n",
      "an input matrix of 8x8 where each element is an integer in the range\n",
      "0..16. This reduces dimensionality and gives invariance to small\n",
      "distortions.\n",
      "\n",
      "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
      "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
      "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
      "1994.\n",
      "\n",
      "References\n",
      "----------\n",
      "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
      "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
      "    Graduate Studies in Science and Engineering, Bogazici University.\n",
      "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
      "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
      "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
      "    Electrical and Electronic Engineering Nanyang Technological University.\n",
      "    2005.\n",
      "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
      "    Algorithm. NIPS. 2000.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(digits.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1233c5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b93c780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gray()  # gray reversed shown below\n",
    "_ = plt.matshow(digits.images[0]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Another way](http://scikit-learn.org/stable/auto_examples/datasets/plot_digits_last_image.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cc239b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(3, 3))\n",
    "plt.imshow(digits.images[0], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cc23ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.matshow(digits.images[108])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  2., 11., 16.,  4.,  0.,  0.,  0.,  0., 12.,  9., 11.,\n",
       "       15.,  1.,  0.,  0.,  0.,  2.,  0.,  4., 16.,  0.,  0.,  0.,  0.,\n",
       "        0.,  2.,  8., 15.,  1.,  0.,  0.,  4., 16., 16., 16., 15.,  7.,\n",
       "        0.,  0.,  3.,  6.,  4., 16.,  3.,  0.,  0.,  0.,  0.,  0.,  6.,\n",
       "       11.,  0.,  0.,  0.,  0.,  0.,  0., 12.,  7.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data[108]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember how we think in machine learning.  We have a multifaceted (multi-featured) set of samples, rows with many columns, and then a single column of correct results, an \"answer key\" if you will.  \n",
    "\n",
    "We often call this answer key column the \"target\" and then measure \"error\" as divergence between guesses and target.  \n",
    "\n",
    "Decreasing divergence bespeaks of a learning rate as the model trains on, or fits the training data.  Whether we control this learning rate as a hyperparameter, or leave it to the algorithm to work at some built-in speed, depends on which machine learner type we've selected.  Below we're looking at [KNN](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) and then a neural net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[108]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a very poor rendering of the numeral 7 and we're immediately forgiving if our Machine Learning algorithm gets some wrong, with training data of such abysmal quality.  As seen from ```digits.data```, the 64 bits used to represent a digit are hardly enough.  Other datasets come with at least 28 x 28 bits for each numeral.  We're truly at the low end with this skimpy number of bits per digit.\n",
    "\n",
    "Neverthesless, we press on... I'm making only minor changes to [this open source script on Github](https://gist.github.com/fabiosato/2a2014cd386d475fd037), by [Fabiosato](https://gist.github.com/fabiosato).\n",
    "\n",
    "Remember how KNN works:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/MDniRwXizWo\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1a1cc61c18>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo(\"MDniRwXizWo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember to distinguish KNN from K-Means.  You might use the latter to create the clusters whereby you could then fit the former.  Here's [a paper on LinkedIn](https://www.linkedin.com/pulse/classification-clustering-knn-vs-k-means-raymond-rashid/) suggesting doing that.  Once you have the clusters (voters), a new data point is \"claimed\" by one or more clusters.  \n",
    "\n",
    "[Hierarchical clustering algorithms](http:github/4dsolutions/Python5/blob/master/OverviewNotes_PYTDS_3.ipynbPYT_DS_Clustering.ipynb) compete with K-Means.  The latter does better for spherical or globular clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/3vHqmPF4VBA\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1a1cbd2da0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo(\"3vHqmPF4VBA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neighbors: 1\n",
      "Confusion Matrix\n",
      "[[60  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 73  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 71  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 70  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 63  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 87  1  0  0  1]\n",
      " [ 0  0  0  0  0  0 76  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 65  0  0]\n",
      " [ 0  2  0  1  0  0  0  0 74  1]\n",
      " [ 0  0  0  2  0  1  0  0  0 71]]\n",
      "Accuracy score: 0.987483\n",
      "\n",
      "Neighbors: 3\n",
      "Confusion Matrix\n",
      "[[60  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 73  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 71  0  0  0  0  0  0  0]\n",
      " [ 0  0  1 69  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 62  0  0  1  0  0]\n",
      " [ 0  1  0  0  0 86  1  0  0  1]\n",
      " [ 0  0  0  0  0  0 76  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 65  0  0]\n",
      " [ 0  2  0  2  0  0  0  0 73  1]\n",
      " [ 0  0  0  1  0  0  0  0  0 73]]\n",
      "Accuracy score: 0.984701\n",
      "\n",
      "Neighbors: 5\n",
      "Confusion Matrix\n",
      "[[60  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 73  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 70  0  0  0  0  1  0  0]\n",
      " [ 0  0  1 69  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 62  0  0  1  0  0]\n",
      " [ 0  0  0  0  0 87  1  0  0  1]\n",
      " [ 0  0  0  0  0  0 76  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 65  0  0]\n",
      " [ 0  5  0  2  0  0  0  0 70  1]\n",
      " [ 0  0  0  1  0  1  0  0  0 72]]\n",
      "Accuracy score: 0.979138\n",
      "\n",
      "Neighbors: 7\n",
      "Confusion Matrix\n",
      "[[60  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 73  0  0  0  0  0  0  0  0]\n",
      " [ 0  1 69  0  0  0  0  1  0  0]\n",
      " [ 0  0  0 70  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 61  0  0  2  0  0]\n",
      " [ 0  0  0  0  1 86  1  0  0  1]\n",
      " [ 0  0  0  0  0  0 76  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 65  0  0]\n",
      " [ 0  5  0  2  0  0  0  0 70  1]\n",
      " [ 0  0  0  0  0  1  0  0  0 73]]\n",
      "Accuracy score: 0.977747\n",
      "\n",
      "Neighbors: 9\n",
      "Confusion Matrix\n",
      "[[60  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 72  0  0  0  0  1  0  0  0]\n",
      " [ 0  1 69  0  0  0  0  1  0  0]\n",
      " [ 0  0  0 70  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 61  0  0  2  0  0]\n",
      " [ 0  0  0  0  1 86  1  0  0  1]\n",
      " [ 0  0  0  0  0  0 76  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 65  0  0]\n",
      " [ 0  5  0  2  0  0  0  1 69  1]\n",
      " [ 0  0  0  1  0  1  0  0  0 72]]\n",
      "Accuracy score: 0.973574\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import neighbors # http://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors\n",
    "\n",
    "# prepare datasets from training and for validation\n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, \n",
    "                                                    test_size=0.4, random_state=0)\n",
    "\n",
    "# runs the kNN classifier for even number of neighbors from 1 to 10\n",
    "for n in range(1, 10, 2):\n",
    "        clf = neighbors.KNeighborsClassifier(n)\n",
    "\n",
    "        # instance based learning\n",
    "        clf.fit(X_train, y_train)\n",
    "\n",
    "        # our 'ground truth'\n",
    "        y_true = y_test\n",
    "\n",
    "        # predict\n",
    "        y_pred = clf.predict(X_test)\n",
    "\n",
    "        # learning metrics\n",
    "        cm = confusion_matrix(y_true, y_pred)\n",
    "        acc = accuracy_score(y_true, y_pred)\n",
    "\n",
    "        print (\"Neighbors: %d\" % n)\n",
    "        print (\"Confusion Matrix\")\n",
    "        print (cm)\n",
    "\n",
    "        print (\"Accuracy score: %f\" % accuracy_score(y_true, y_pred))\n",
    "        print ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Discerning digits within a blizzard of data points streaming in, or other patterns, may be described as a process of identifying clusters or [neighborhoods](http://scikit-learn.org/stable/modules/neighbors.html).  Even before we name the clusters we claim to find, we need to find them, and this is where dimensionality reduction comes in handy, as if we can get the dimensions down to three, we have some axes we might use.\n",
    "\n",
    "\"Dimensionality reduction\" involves finding eigenvectors, the most efficient at singling out cells in not containing redundant info, forming a basis.  An idea of ranking eigenvectors, in the sense of \"most significant digits\", allows us to cluster data by just the first few eigenvector coordinates.\n",
    "\n",
    "One might usefully compare this process to discovering the desmomap, or binary tree resulting from bottom-up progressive agglomeration into larger groups.  One may then place a threshold cut through the data to vary the number of clusters one wishes to regard as separate.  There's a sense of binning and/or pigeon-holing, where the hyperparameter is the degree of subdivisioning.\n",
    "\n",
    "Does a [neural network](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html) fare better?  Let's admit, the KNN machine learner did a great job.  Fast horse!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "[[60  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 72  0  0  0  0  0  0  1  0]\n",
      " [ 0  1 68  0  0  0  0  0  2  0]\n",
      " [ 0  0  0 68  0  0  0  0  1  1]\n",
      " [ 0  0  0  0 63  0  0  0  0  0]\n",
      " [ 0  0  0  2  1 84  1  0  0  1]\n",
      " [ 0  1  0  0  0  1 74  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 65  0  0]\n",
      " [ 0  5  1  1  0  1  0  0 68  2]\n",
      " [ 1  0  0  2  0  1  0  0  0 70]]\n",
      "Accuracy score: 0.962448\n",
      "\n",
      "Confusion Matrix\n",
      "[[60  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 72  0  0  0  0  0  0  1  0]\n",
      " [ 0  1 68  0  0  0  0  1  1  0]\n",
      " [ 0  0  1 68  0  1  0  0  0  0]\n",
      " [ 0  0  0  0 63  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 84  1  0  0  4]\n",
      " [ 0  1  0  0  0  1 74  0  0  0]\n",
      " [ 0  0  0  0  1  0  0 64  0  0]\n",
      " [ 0  4  0  1  0  0  0  0 73  0]\n",
      " [ 0  0  1  1  0  1  0  0  0 71]]\n",
      "Accuracy score: 0.969402\n",
      "\n",
      "Confusion Matrix\n",
      "[[60  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 69  0  0  0  0  1  0  2  1]\n",
      " [ 0  1 70  0  0  0  0  0  0  0]\n",
      " [ 0  0  1 68  0  1  0  0  0  0]\n",
      " [ 0  0  0  0 62  0  0  1  0  0]\n",
      " [ 0  0  0  0  1 86  1  0  0  1]\n",
      " [ 0  1  0  0  0  1 74  0  0  0]\n",
      " [ 0  1  0  0  0  0  0 64  0  0]\n",
      " [ 0  3  1  0  1  1  0  0 71  1]\n",
      " [ 0  0  0  1  0  1  0  0  0 72]]\n",
      "Accuracy score: 0.968011\n",
      "\n",
      "Confusion Matrix\n",
      "[[60  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 71  0  0  0  0  0  0  1  1]\n",
      " [ 0  2 68  0  0  0  0  0  1  0]\n",
      " [ 0  0  0 67  0  1  0  0  2  0]\n",
      " [ 0  0  0  0 61  0  0  2  0  0]\n",
      " [ 0  0  0  1  0 85  2  0  0  1]\n",
      " [ 1  1  0  0  0  0 74  0  0  0]\n",
      " [ 0  0  0  0  2  0  0 63  0  0]\n",
      " [ 0  1  0  0  1  0  0  0 74  2]\n",
      " [ 0  0  0  1  0  1  0  1  0 71]]\n",
      "Accuracy score: 0.965229\n",
      "\n",
      "Confusion Matrix\n",
      "[[60  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 70  0  0  0  0  0  0  2  1]\n",
      " [ 0  1 68  1  0  0  0  0  1  0]\n",
      " [ 0  0  0 68  0  1  0  0  0  1]\n",
      " [ 0  0  0  0 61  0  0  2  0  0]\n",
      " [ 0  1  0  0  0 85  2  0  0  1]\n",
      " [ 0  1  0  0  0  1 74  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 65  0  0]\n",
      " [ 0  3  0  0  0  0  0  1 71  3]\n",
      " [ 0  0  0  1  0  2  0  1  0 70]]\n",
      "Accuracy score: 0.962448\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# runs the MLP classifier, all with same hyperparameters\n",
    "for n in range(1, 10, 2):\n",
    "        clf = MLPClassifier()\n",
    "\n",
    "        # instance based learning\n",
    "        clf.fit(X_train, y_train)\n",
    "\n",
    "        # our 'ground truth'\n",
    "        y_true = y_test\n",
    "\n",
    "        # predict\n",
    "        y_pred = clf.predict(X_test)\n",
    "\n",
    "        # learning metrics\n",
    "        cm = confusion_matrix(y_true, y_pred)\n",
    "        acc = accuracy_score(y_true, y_pred)\n",
    "\n",
    "        # print (\"Neighbors: %d\" % n)\n",
    "        print (\"Confusion Matrix\")\n",
    "        print (cm)\n",
    "\n",
    "        print (\"Accuracy score: %f\" % accuracy_score(y_true, y_pred))\n",
    "        print ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'd say these two are competitive, but award KNN first prize in this case. On the other hand, I did not try varying the hyperparameters available to me with [the MLP classifier](http://scikit-learn.org/stable/modules/neural_networks_supervised.html).  Lets say the results so far are inconclusive.  More research needed."
   ]
  }
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