09. Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest\n",
    "- Ensemble Algorithm\n",
    "- model made up of many decision trees, which are independent of/uncorrelated to each other\n",
    "- because of no/little correlation between trees provides randomness, due to which impact of error from one tree dont influence other trees\n",
    "\n",
    "**Key Concepts:**\n",
    "- While building trees it performs random sampling of training data points\n",
    "- While splitting nodes it performs random subsets of features \n",
    "- Shallow trees(less depth) have less variance and higher bias - better choice for sequential(boosting)\n",
    "- Deep trees low bias and higher variance - better choice for bagging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random sampling of training observations\n",
    "While Training:\n",
    "- each tree in a random forest learns from a random sample of the data points\n",
    "- BOOTSTRAPPING - means that some samples will be used multiple times in a single tree\n",
    "- each tree might have high variance w.r.t to a particular training data, however overall variance will be low without increasing the bias\n",
    "\n",
    "While Testing:\n",
    "- predictions are made by averaging the predictions of each decision tree\n",
    "- BAGGING - procedure of training each individual learner on different bootstrapped subsets of the data and then averaging the predictions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Subsets of features for splitting nodes\n",
    "- subset of all the features are considered for splitting each node in each decision tree\n",
    "- generally this subset is equal to sqrt(n_features)\n",
    "- E.g: Total 25 features at each node in each tree, only 5 features will be considered in splitting the node\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Understanding Random Forest(Example)\n",
    "- to predict a company A stock will go up or down\n",
    "- a team of analysts is formed having no idea about the company A\n",
    "- Each analyst has low bias as they dont have any prior assumption\n",
    "- they will learn from news reports datasets\n",
    "- These news reports may have high noise or irrelevant data\n",
    "- analysts are prone to capture these noise\n",
    "- each analyst might have different prediction with high variance based on different training set of reports\n",
    "- allow each analyst access to section of reports\n",
    "- the noise in these section will cancelled out while sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9835725318711682\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest Regression\n",
    "\n",
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Data/Regression/Company_Performance.csv')\n",
    "X = dataset.iloc[:, 0:1].values\n",
    "y = dataset.iloc[:, 1].values\n",
    "\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "\n",
    "# Feature Scaling\n",
    "\"\"\"from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)\n",
    "sc_y = StandardScaler()\n",
    "y_train = sc_y.fit_transform(y_train)\"\"\"\n",
    "\n",
    "# Fitting Random Forest Regression to the dataset\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_r = RandomForestRegressor(n_estimators = 300, random_state = 0)\n",
    "rf_r.fit(X, y)\n",
    "\n",
    "y_pred = rf_r.predict(X_test)\n",
    "\n",
    "print(rf_r.score(X_test, y_test))\n",
    "\n",
    "\n",
    "# Visualising the Random Forest Regression results (higher resolution)\n",
    "#Graph \n",
    "X_dt = np.arange(min(X), max(X), 0.1)\n",
    "X_dt = X_dt.reshape(len(X_dt), 1)\n",
    "plt.scatter(X, y, color = 'black')\n",
    "plt.plot(X_dt, rf_r.predict(X_dt), color = 'red' )\n",
    "plt.title('Random Forest Regression')\n",
    "plt.ylabel('No. of employees')\n",
    "plt.xlabel('No. Years')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9416909620991254\n"
     ]
    }
   ],
   "source": [
    "#Import Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "dataset = pd.read_csv('Data/Classification/BankNote_Authentication.csv')\n",
    "\n",
    "X = dataset.iloc[:, [0,1]].values\n",
    "y = dataset.iloc[:, 4].values\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
    "\n",
    "# Fitting Random Forest Regression to the dataset\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_c = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "rf_c.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the Test set results\n",
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = rf_c.predict(X_test)\n",
    "test_acc = accuracy_score(y_test, y_pred)\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np\n",
    "#Define Variables\n",
    "clf = rf_c\n",
    "h = 0.01\n",
    "X_plot, z_plot = X_test, y_test\n",
    "\n",
    "#Standard Template to draw graph\n",
    "x_min, x_max = X_plot[:, 0].min() - 1, X_plot[:, 0].max() + 1\n",
    "y_min, y_max = X_plot[:, 1].min() - 1, X_plot[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                     np.arange(y_min, y_max, h))\n",
    "\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh\n",
    "Z = clf.predict(np.array([xx.ravel(), yy.ravel()]).T)\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.contourf(xx, yy, Z,\n",
    "             alpha = 0.7, cmap = ListedColormap(('red', 'green')))\n",
    "\n",
    "\n",
    "for i, j in enumerate(np.unique(z_plot)):\n",
    "    plt.scatter(X_plot[z_plot == j, 0], X_plot[z_plot == j, 1],\n",
    "                c = ['red', 'green'][i], cmap = ListedColormap(('red', 'green')), label = j)\n",
    "   #X[:, 0], X[:, 1] \n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.title('Random Forest')\n",
    "plt.xlabel('variance')\n",
    "plt.ylabel('skewness')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advantages\n",
    "* its robust for missing and erroneous data as well as insufficient information with good performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions and Answers\n",
    "**Ques:** While taking samples randomly in random forest for making individual decision trees, some of the entries are duplicates. DOes this creates problem?\n",
    "**Ans**:  \n",
    "- It will be considered as “Row sampling without replacement”.When a row is added, it means a new data point is added in the feature space. The model will only try to learn from this data point.  \n",
    "- Problem happens when the same feature is added twice, it means a dimension will overlap another dimension. It technically makes no sense but just increases the computation of the algorithm.  \n",
    "- CRicket Balls Box: Adding same row/reading is like adding ball. No issue with this. But if we add a plate in the base of box it is of no use(like adding duplicate feature)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Research Paper:  \n",
    "https://www.hindawi.com/journals/jam/2012/258054/    "
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}