02_scikit-learn-api/02-2_exercise.ipynb

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   "source": [
    "# 02 -- Exercise (The scikit-learn API)"
   ]
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   "source": [
    "%load_ext watermark"
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     "text": [
      "Author: Sebastian Raschka\n",
      "\n",
      "numpy       : 1.25.0\n",
      "scikit-learn: 1.3.0\n",
      "mlxtend     : 0.22.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -a \"Sebastian Raschka\" -p numpy,scikit-learn,mlxtend"
   ]
  },
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   "cell_type": "markdown",
   "id": "b153f6da-d87d-457b-8533-7e80dc239129",
   "metadata": {},
   "source": [
    "---"
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   "metadata": {},
   "source": [
    "# Given Code"
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   "id": "39f90225-fefa-489f-a848-48558723a1fc",
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       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "X, y = load_iris(return_X_y=True)\n",
    "est = KNeighborsClassifier()\n",
    "\n",
    "est.fit(X, y)"
   ]
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    "est.predict(X)"
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    "est.score(X, y)"
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  {
   "cell_type": "markdown",
   "id": "c890e8fa-5856-4485-af95-675faefd5689",
   "metadata": {},
   "source": [
    "# Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab3c852-78f8-4df1-8bac-c2ea2703c25a",
   "metadata": {},
   "source": [
    "1. Split the data into 3 partitions:\n",
    "    - 50% training data\n",
    "    - 25% validation data\n",
    "    - 25% test data\n",
    "\n",
    "Hint: Import the `train_test_split` method (and use a `random_state=999` so we can compare results)\n",
    "\n",
    "```python\n",
    "from sklearn.model_selection import train_test_split\n",
    "```\n",
    "<br>\n",
    "\n",
    "<hr>\n",
    "\n",
    "<br>\n",
    "\n",
    "2. Then, train the `KNeighborsClassifier()` on the training set, evaluate it on the validation set\n",
    "\n",
    "<br>\n",
    "\n",
    "<hr>\n",
    "\n",
    "<br>\n",
    "\n",
    "3. Change the number of nearest neighbors\n",
    "    - Find out what the default number of nearest neighbors is\n",
    "    - Then change this number to 3 nearest neighbors\n",
    "    - Train the model on the training set\n",
    "    - evaluate it on the validation set\n",
    "  \n",
    "<br>\n",
    "\n",
    "<hr>\n",
    "\n",
    "<br>\n",
    "\n",
    "4. Pick the best value for the nearest neighbors based on the validation set performance\n",
    "\n",
    "<br>\n",
    "\n",
    "5. Evaluate the model with the best \"nearest neighbors\" hyperparameter on the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a92cc66-dbc7-415a-9bca-e0fba60e5766",
   "metadata": {},
   "source": [
    "**Hint: Use the `?` mark in Jupyter to get more information about the individual classes, functions, and methods.**\n",
    "\n",
    "E.g., run \n",
    "\n",
    "```python\n",
    "train_test_split?\n",
    "```\n",
    "\n",
    "in a new cell.\n",
    "\n",
    "<br>\n",
    "<hr>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cbdc25b-aad6-4caa-9880-4708ef2d8e02",
   "metadata": {},
   "source": [
    "### 1. Split the data into 3 partitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b0b54c4-8031-47b2-b5f2-00fc2a3fe66a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f474055-0c27-4a19-8516-5ddfdb3b68b6",
   "metadata": {},
   "source": [
    "### 2. Train the `KNeighborsClassifier()` on the training set, evaluate it on the validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9243eb28-71a6-47a1-92f9-b784f97b2f5f",
   "metadata": {},
   "source": [
    "### 3. Change the number of nearest neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a32fb571-da7f-4b3c-ae2c-ae92c07ee78f",
   "metadata": {},
   "source": [
    "### 4. Pick the best value for the nearest neighbors based on the validation set performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df0bc198-1e67-4d81-8861-e2e255ff24bf",
   "metadata": {},
   "source": [
    "### 5. Evaluate the model with the best \"nearest neighbors\" hyperparameter on the test set"
   ]
  }
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