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    "# SECOND ASSIGNMENT. MACHINE LEARNING WITH SCIKIT-LEARN. \n",
    "## PART I (1.5 POINTS)\n",
    "\n",
    "The aim of part I of the Scikit-learn assignment is for you to self-learn and get used to this Machine Learning tool. The main part (part II) of the assignment will be explained next week (11/12). \n",
    "\n",
    "Here, you will learn to:\n",
    "\n",
    "- Perform a crossvalidation on the iris classification problem with decision trees (so far, we have only done regression)\n",
    "- Perform a crossvalidation on the iris classification problem **with KNN** (I haven't explained this, you will have to learn how to use it from the web)\n",
    "- Perform grid search in order to determine the best value for hyper-parameter K\n",
    "\n",
    "You will also have to go through two notebooks I have prepared for you in order to see how crossvalidation and hyper-parameter tuning are used in Scikit-learn\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Carry out the \"DECISION TREES WITH A TRAINING AND A TESTING SET AND CROSSVALIDATION\" notebook and understand the main ideas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Perform a crossvalidation on the iris classification problem with decision trees:\n",
    "\n",
    "** It is important to remember that for classification, you have to use**\n",
    "- clf = tree.DecisionTreeClassifier() # for constructing the classifier\n",
    "- metrics.accuracy # for computing error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "# Write code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Perform a crossvalidation on the iris classification problem with KNN\n",
    "\n",
    "I haven't explained how to use KNN in Scikit-learn. You will have to read and obtain the relevant information [here](http://scikit-learn.org/stable/modules/neighbors.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# Write code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Try different values for K (KNN) - change them by hand- and see if you obtain a better result than with KNN default value. Always use crossvalidation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Write code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Carry out THE \"DECISION TREE HYPER-PARAMETERS. TUNING DECISION TREES\" notebook and understand the main ideas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. USE GRID SEARCH AND RANDOMIZED SEARCH TO FIND THE OPTIMAL VALUE FOR K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Write code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. OPTIONAL (you may get 0.25 extra points if you decide to do this). \n",
    "\n",
    "K is the main hyper-parameter of KNN. Find another hyper-parameter that you consider relevant, and try to optimize both K and the other parameter using grid-search. Are you able to improve on previous results?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Write code here (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
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