11.3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sklearn\n",
      "  Downloading https://files.pythonhosted.org/packages/1e/7a/dbb3be0ce9bd5c8b7e3d87328e79063f8b263b2b1bfa4774cb1147bfcd3f/sklearn-0.0.tar.gz\n",
      "Requirement already satisfied: scikit-learn in d:\\opensources\\anaconda3\\install\\lib\\site-packages (from sklearn) (0.21.2)\n",
      "Requirement already satisfied: scipy>=0.17.0 in d:\\opensources\\anaconda3\\install\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\n",
      "Requirement already satisfied: joblib>=0.11 in d:\\opensources\\anaconda3\\install\\lib\\site-packages (from scikit-learn->sklearn) (0.13.2)\n",
      "Requirement already satisfied: numpy>=1.11.0 in d:\\opensources\\anaconda3\\install\\lib\\site-packages (from scikit-learn->sklearn) (1.16.4)\n",
      "Building wheels for collected packages: sklearn\n",
      "  Building wheel for sklearn (setup.py): started\n",
      "  Building wheel for sklearn (setup.py): finished with status 'done'\n",
      "  Stored in directory: C:\\Users\\Administrator\\AppData\\Local\\pip\\Cache\\wheels\\76\\03\\bb\\589d421d27431bcd2c6da284d5f2286c8e3b2ea3cf1594c074\n",
      "Successfully built sklearn\n",
      "Installing collected packages: sklearn\n",
      "Successfully installed sklearn-0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_data = np.random.random((20, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.73809332, 0.10227724],\n",
       "       [0.49275198, 0.12891189],\n",
       "       [0.62854845, 0.37595189],\n",
       "       [0.7709408 , 0.15476334],\n",
       "       [0.90073677, 0.56728099],\n",
       "       [0.51904753, 0.77493461],\n",
       "       [0.99634136, 0.7383026 ],\n",
       "       [0.86332927, 0.02286471],\n",
       "       [0.74765785, 0.37888752],\n",
       "       [0.45378872, 0.35882426],\n",
       "       [0.9578673 , 0.18099627],\n",
       "       [0.61016226, 0.66263579],\n",
       "       [0.5084412 , 0.97934241],\n",
       "       [0.94680038, 0.10121926],\n",
       "       [0.13914563, 0.2264028 ],\n",
       "       [0.62830303, 0.1079369 ],\n",
       "       [0.80643694, 0.97248342],\n",
       "       [0.51388186, 0.59278109],\n",
       "       [0.80360915, 0.29956097],\n",
       "       [0.22064445, 0.01240332]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = random_data[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = random_data[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assmuing_function(x):\n",
    "    # 体重 -> 高血压的概率\n",
    "    # 收入 -> 买阿玛尼的概率\n",
    "    # 其实都是一种潜在的函数关系 + 一个随机变化\n",
    "    return 13.4 * x + 5 + random.randint(-5, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = [assmuing_function(x) for x in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f0f9074908>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression().fit(X.reshape(-1, 1), y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3805494604363726"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X.reshape(-1, 1), y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.58501342])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.327460722127682"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x): \n",
    "    return reg.coef_ * x + reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f0fb34dd30>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y)\n",
    "plt.plot(X, f(X), color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###实现KNN模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X, y):\n",
    "    return [(Xi, yi) for Xi, yi in zip(X, y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import cosine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(x1, x2):\n",
    "    return cosine(x1, x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(x, k=5):\n",
    "    most_similars = sorted(model(X, y), key=lambda xi: distance(xi[0], x))[:k]\n",
    "    # 已经获得了最相似的数据集\n",
    "    # 然后呢，Counter() -> most_common() -> 就可以获得出现最多的这个y了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###实现决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def entropy(elements):\n",
    "    '''熵指的是群体的混乱程度'''\n",
    "    counter = Counter(elements)\n",
    "    probs = [counter[c] / len(elements) for c in set(elements)]\n",
    "    return - sum(p * np.log(p) for p in probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy([1,1,1,1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5623351446188083"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy([1,1,1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3862943611198906"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0397207708399179"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy([1,2,3,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##决策树怎么样来决定，哪一个特征来进行分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "mock_data = {\n",
    "    'gender' : ['F','F','F','F','M','M','M'],\n",
    "    'income' : ['+10','-10','+10','+10','+10','+10','-10'],\n",
    "    'family_number' : [1,1,2,1,1,1,2],\n",
    "    'bought' : [1,1,1,0,0,0,1],   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import  pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.DataFrame.from_dict(mock_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>family_number</th>\n",
       "      <th>bought</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>+10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M</td>\n",
       "      <td>-10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender income  family_number  bought\n",
       "0      F    +10              1       1\n",
       "1      F    -10              1       1\n",
       "2      F    +10              2       1\n",
       "3      F    +10              1       0\n",
       "4      M    +10              1       0\n",
       "5      M    +10              1       0\n",
       "6      M    -10              2       1"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##决策树在选择决策过程，决策顺序的时候，其实是按照根据数据特征进行分割之后，数据的熵最少原则进行的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(mock_data['family_number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_split_1 = dataset[dataset['family_number'] == 1]['bought'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 0, 0, 0]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_split_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_split_2 = dataset[dataset['family_number'] != 1]['bought'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6730116670092565"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy(sub_split_1) + entropy(sub_split_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_split_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "_sub_split_2 = dataset[dataset['gender'] != 'M']['bought'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "_sub_split_1 = dataset[dataset['gender'] != 'F']['bought'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 1]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_sub_split_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 0]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_sub_split_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.198849312913621"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy(_sub_split_1) + entropy(_sub_split_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gender', 'income', 'family_number', 'bought']"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'icecream'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-110-58dadeb530cd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0micecream\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mic\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'icecream'"
     ]
    }
   ],
   "source": [
    "from icecream import ic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_the_min_spilter(training_data: pd.DataFrame, target: str) -> str:\n",
    "    x_fields = set(training_data.columns.tolist()) - {target}\n",
    "    \n",
    "    spliter = None\n",
    "    min_entropy = float('inf')\n",
    "    \n",
    "    for f in x_fields:\n",
    "        ic(f)\n",
    "        values = set(training_data[f])\n",
    "        ic(values)\n",
    "        for v in values:\n",
    "            sub_spliter_1 = training_data[training_data[f] == v][target].tolist()\n",
    "            ic(sub_split_1)\n",
    "            entropy_1 = entropy(sub_spliter_1)\n",
    "            ic(entropy_1)\n",
    "            sub_spliter_2 = training_data[training_data[f] != v][target].tolist()\n",
    "            ic(sub_split_2)\n",
    "            entropy_2 = entropy(sub_spliter_2)\n",
    "            ic(entropy_2)\n",
    "            entropy_v = entropy_1 + entropy_2\n",
    "            ic(entropy_v)\n",
    "            \n",
    "            if entropy_v <= min_entropy:\n",
    "                min_entropy = entropy_v\n",
    "                spliter = (f, v)\n",
    "    \n",
    "    print('spliter is: {}'.format(spliter))\n",
    "    print('the min entropy is: {}'.format(min_entropy))\n",
    "    \n",
    "    return spliter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ic' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-112-0ab0ad5ec5b8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfind_the_min_spilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bought'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-111-00dc0e47b049>\u001b[0m in \u001b[0;36mfind_the_min_spilter\u001b[1;34m(training_data, target)\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mx_fields\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m         \u001b[0mic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[0mic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'ic' is not defined"
     ]
    }
   ],
   "source": [
    "find_the_min_spilter(dataset, 'bought')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>family_number</th>\n",
       "      <th>bought</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>+10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M</td>\n",
       "      <td>-10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender income  family_number  bought\n",
       "2      F    +10              2       1\n",
       "6      M    -10              2       1"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset['family_number'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>family_number</th>\n",
       "      <th>bought</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender income  family_number  bought\n",
       "0      F    +10              1       1\n",
       "1      F    -10              1       1\n",
       "3      F    +10              1       0\n",
       "4      M    +10              1       0\n",
       "5      M    +10              1       0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset['family_number'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ic' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-115-be2288e66281>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfind_the_min_spilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'family_number'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bought'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-111-00dc0e47b049>\u001b[0m in \u001b[0;36mfind_the_min_spilter\u001b[1;34m(training_data, target)\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mx_fields\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m         \u001b[0mic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[0mic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'ic' is not defined"
     ]
    }
   ],
   "source": [
    "find_the_min_spilter(dataset[dataset['family_number'] == 1], 'bought')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = dataset[dataset['family_number'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>family_number</th>\n",
       "      <th>bought</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender income  family_number  bought\n",
       "4      M    +10              1       0\n",
       "5      M    +10              1       0"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df[sub_df['gender'] == 'M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>family_number</th>\n",
       "      <th>bought</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>+10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender income  family_number  bought\n",
       "0      F    +10              1       1\n",
       "1      F    -10              1       1\n",
       "3      F    +10              1       0"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df[sub_df['gender'] != 'M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ic' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-119-03322bf9d0bd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfind_the_min_spilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msub_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msub_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'gender'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'M'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bought'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-111-00dc0e47b049>\u001b[0m in \u001b[0;36mfind_the_min_spilter\u001b[1;34m(training_data, target)\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mx_fields\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m         \u001b[0mic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[0mic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'ic' is not defined"
     ]
    }
   ],
   "source": [
    "find_the_min_spilter(sub_df[sub_df['gender'] != 'M'], 'bought')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# #kmeans的简单例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [random.randint(0, 100) for _ in range(100)]\n",
    "Y = [random.randint(0, 100) for _ in range(100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f0fb6b7588>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "tranning_data = [[x, y] for x, y in zip(X, Y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster = KMeans(n_clusters=6, max_iter=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=500,\n",
       "       n_clusters=6, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster.fit(tranning_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[56.45454545, 80.27272727],\n",
       "       [80.30769231, 20.19230769],\n",
       "       [27.5       , 44.95      ],\n",
       "       [90.33333333, 74.22222222],\n",
       "       [29.75      , 11.16666667],\n",
       "       [18.27272727, 81.72727273]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 1, 1, 2, 2, 1, 4, 2, 5, 5, 1, 4, 2, 4, 1, 3, 3, 1, 2, 4, 5,\n",
       "       0, 4, 2, 2, 0, 4, 2, 0, 2, 5, 1, 4, 5, 1, 1, 4, 0, 1, 0, 1, 2, 2,\n",
       "       4, 0, 0, 0, 5, 2, 1, 1, 2, 1, 0, 0, 0, 0, 0, 3, 4, 4, 2, 0, 1, 1,\n",
       "       0, 1, 2, 5, 3, 1, 0, 0, 1, 5, 4, 3, 3, 2, 1, 0, 5, 1, 5, 0, 1, 1,\n",
       "       0, 2, 5, 1, 2, 0, 3, 0, 2, 3, 1, 1])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "centers = defaultdict(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "for label, location in zip(cluster.labels_, tranning_data):\n",
    "    centers[label].append(location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = ['red', 'green', 'grey', 'black', 'yellow', 'orange']\n",
    "\n",
    "for i, c in enumerate(centers):\n",
    "    for location in centers[c]:\n",
    "        plt.scatter(*location, c=color[i])\n",
    "        \n",
    "for center in cluster.cluster_centers_:\n",
    "    plt.scatter(*center, s=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 问答\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " What's the model? why all the models are wrong, but some are useful?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "通过主观意识借助实体或者虚拟表现构成客观阐述形态结构的一种表达目的的物件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful  \n",
    "大多数模型都具有不一样类型的拟合或误差的度量。根据所使用的建模的类型，可以有不同的拟合方式和误差\n",
    "模型就是对实际事物的一个抽象，但事实上，实际事物的影响因素是多种多样的，一个数学模型只可能考虑其中的一部分影响因素而不是全部，但事物的发展有时却的确由不显著因素影响，数学模型与事物实际的规律还是有很大差异的，即所谓错误的。但是，由于数学模型往往抓住的和考虑的是对事物发展影响最为显著的一些因素，所以，数学模型抓住了事物发展的主要矛盾和主要规律，从这个意义上讲，数学模型还是有用滴。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "What's the underfitting and overfitting? List the reasons that could make model overfitting or underfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "拟合的函数和训练集误差较大，就是欠拟合\n",
    "拟合的函数完美的匹配训练集数据，就是过拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "欠拟合原因：\n",
    "\n",
    "    模型复杂度过低\n",
    "    特征量过少\n",
    "解决方法：\n",
    "1.增加新特征，可以考虑加入进特征组合、高次特征，来增大假设空间\n",
    "2.添加多项式特征，这个在机器学习算法里面用的很普遍，例如将线性模型通过添加二次项或者三次项使模型泛化能力更强\n",
    "3.减少正则化参数，正则化的目的是用来防止过拟合的，但是模型出现了欠拟合，则需要减少正则化参数\n",
    "4.使用非线性模型，比如核SVM 、决策树、深度学习等模型\n",
    "5.调整模型的容量(capacity)，通俗地，模型的容量是指其拟合各种函数的能力\n",
    "6.容量低的模型可能很难拟合训练集；使用集成学习方法，如Bagging ,将多个弱学习器Bagging\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "过拟合原因：\n",
    "1.建模样本选取有误，如样本数量太少，选样方法错误，样本标签错误等，导致选取的样本数据不足以代表预定的分类规则\n",
    "2.样本噪音干扰过大，使得机器将部分噪音认为是特征从而扰乱了预设的分类规则\n",
    "3.假设的模型无法合理存在，或者说是假设成立的条件实际并不成立\n",
    "4.参数太多，模型复杂度过高\n",
    "5.对于决策树模型，如果我们对于其生长没有合理的限制，其自由生长有可能使节点只包含单纯的事件数据(event)或非事件数据(no event)，使其虽然可以完美匹配（拟合）训练数据，但是无法适应其他数据集\n",
    "6.对于神经网络模型：a)对样本数据可能存在分类决策面不唯一，随着学习的进行,，BP算法使权值可能收敛过于复杂的决策面；b)权值学习迭代次数足够多(Overtraining)，拟合了训练数据中的噪声和训练样例中没有代表性的特征\n",
    "解决方法：\n",
    "\n",
    "    正则化（Regularization）（L1和L2）\n",
    "    数据扩增，即增加训练数据样本\n",
    "    Dropout\n",
    "    Early stopping\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " What's the precision, recall, AUC, F1, F2score. What are they mainly target on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision是最常用最基本的评价指标，但在二元分类且正反例不平衡的情况下，没什么用\n",
    "recall是相对真实的答案而言，你的模型预测到的值占真实值的比例\n",
    "F1-score 就是一个综合考虑precision和recall的标准\n",
    "综合考虑Precision和Recall的调和值\n",
    "当 beta =1时，成为F1-Score，这时召回率和精确率都很重要，权重相同\n",
    "我们认为召回率更加重要，那就调整 β的值大于1，就是F2-Score\n",
    "AUC只能用于二分类模型的评估，AUC即ROC曲线下的面积，而ROC曲线的横轴是FPRate，纵轴是TPRate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Based on our course and yourself mind, what's the machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "让机器学习是从数据中提取知识，是统计学、人工智能和计算机科学的交叉领域，也被称为预测分析或统计学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "正确定义了机器学习模型的评价标准(evaluation)， 问题基本上就已经解决一半\". 这句话是否正确？你是怎么看待的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "机器学习过程包括两个阶段：原型设计阶段（Prototyping）与应用阶段（Deployed）\n",
    "这两个阶段使用的数据集也不一样，分别是历史数据（historical data）与新数据（live data）\n",
    "在机器学习中，很多模型都是假设数据的分布是一定的，不变的，即历史数据与将来的数据都服从相同的分布。但是，在现实生活中，这种假设往往是不成立的，即数据的分布会随着时间的移动而改变，有时甚至变化得很急剧，这种现象称为分布漂移（Distribution Drift）\n",
    "不同的机器学习任务有着不同的性能评价指标\n",
    "所以原型阶段中最重要的任务便是选择一个正确的适合的模型对数据进行拟合\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}