notebooks/ML/Principal Component Analysis/PCA - NOTES - IRIS DATA_SETS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from random import random\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PCA - NOTES - IRIS DATA_SETS\n",
    "iris_data,iris_label = load_iris().data,load_iris().target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.shape\n",
    "#da= pd.DataFrame(iris_data,columns=iris_label.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.9</td>\n",
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       "      <td>6.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.9</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.7</td>\n",
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       "      <td>3.3</td>\n",
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       "      <td>1.4</td>\n",
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       "      <td>1.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
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       "      <td>2.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.3</td>\n",
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     },
     "execution_count": 27,
     "metadata": {},
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   ],
   "source": [
    "da.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA()\n",
    "pca.fit(iris_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "tranformed_data= pca.transform(iris_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "       [ 5.88006443e-01, -4.84287420e-01,  4.44433499e-01,\n",
       "         2.50976060e-01],\n",
       "       [ 8.06858313e-01,  1.94182315e-01,  3.88963063e-01,\n",
       "         1.14207243e-01],\n",
       "       [ 1.22069088e+00,  4.07619594e-01, -2.37167010e-01,\n",
       "        -3.12171829e-02],\n",
       "       [ 8.15095236e-01, -3.72037060e-01, -6.14720843e-01,\n",
       "        -1.54021000e-01],\n",
       "       [ 2.45957680e-01, -2.68524397e-01,  1.88366812e-01,\n",
       "         1.46674512e-01],\n",
       "       [ 1.66413217e-01, -6.81926725e-01, -6.00092259e-02,\n",
       "        -2.96222195e-02],\n",
       "       [ 4.64800288e-01, -6.70711545e-01, -2.43068557e-02,\n",
       "         2.69651428e-01],\n",
       "       [ 8.90815198e-01, -3.44644444e-02, -9.94693289e-03,\n",
       "         1.53484666e-01],\n",
       "       [ 2.30548024e-01, -4.04385848e-01, -2.29410241e-01,\n",
       "        -1.69303245e-02],\n",
       "       [-7.04531759e-01, -1.01224823e+00, -1.05691149e-01,\n",
       "        -4.56133071e-02],\n",
       "       [ 3.56981495e-01, -5.04910093e-01,  1.66171702e-02,\n",
       "         9.87414793e-02],\n",
       "       [ 3.31934480e-01, -2.12654684e-01,  8.32042909e-02,\n",
       "         2.38475434e-01],\n",
       "       [ 3.76215651e-01, -2.93218929e-01,  7.79963511e-02,\n",
       "         1.31137381e-01],\n",
       "       [ 6.42576008e-01,  1.77381901e-02, -2.05394967e-01,\n",
       "         2.13776830e-02],\n",
       "       [-9.06469865e-01, -7.56093367e-01, -1.25996476e-02,\n",
       "        -2.32534844e-01],\n",
       "       [ 2.99000842e-01, -3.48897806e-01,  1.05816605e-02,\n",
       "         5.11811717e-02],\n",
       "       [ 2.53119273e+00, -9.84910950e-03,  7.60165427e-01,\n",
       "         2.90555728e-02],\n",
       "       [ 1.41523588e+00, -5.74916348e-01,  2.96322527e-01,\n",
       "         1.53046739e-02],\n",
       "       [ 2.61667602e+00,  3.43903151e-01, -1.10787883e-01,\n",
       "        -6.57720412e-02],\n",
       "       [ 1.97153105e+00, -1.79727904e-01,  1.08424662e-01,\n",
       "         2.36790934e-01],\n",
       "       [ 2.35000592e+00, -4.02609471e-02,  2.85389563e-01,\n",
       "         1.70633278e-04],\n",
       "       [ 3.39703874e+00,  5.50836673e-01, -3.48437556e-01,\n",
       "         1.12371653e-01],\n",
       "       [ 5.21232244e-01, -1.19275873e+00,  5.45659296e-01,\n",
       "         9.81266196e-02],\n",
       "       [ 2.93258707e+00,  3.55500003e-01, -4.20239936e-01,\n",
       "         2.57191032e-01],\n",
       "       [ 2.32122882e+00, -2.43831502e-01, -3.48304395e-01,\n",
       "         7.86746130e-02],\n",
       "       [ 2.91675097e+00,  7.82791949e-01,  4.23335418e-01,\n",
       "        -1.10982071e-01],\n",
       "       [ 1.66177415e+00,  2.42228408e-01,  2.42440190e-01,\n",
       "        -1.21040552e-01],\n",
       "       [ 1.80340195e+00, -2.15637617e-01, -3.76481682e-02,\n",
       "        -7.80198444e-02],\n",
       "       [ 2.16559180e+00,  2.16275585e-01,  3.33266418e-02,\n",
       "        -1.63061478e-01],\n",
       "       [ 1.34616358e+00, -7.76818347e-01,  2.81902882e-01,\n",
       "        -1.40440869e-01],\n",
       "       [ 1.58592822e+00, -5.39640714e-01,  6.29029326e-01,\n",
       "        -3.29551728e-01],\n",
       "       [ 1.90445637e+00,  1.19250692e-01,  4.79639820e-01,\n",
       "        -2.19621263e-01],\n",
       "       [ 1.94968906e+00,  4.19432597e-02,  4.41861676e-02,\n",
       "         1.57681907e-01],\n",
       "       [ 3.48705536e+00,  1.17573933e+00,  1.33894874e-01,\n",
       "         3.09219573e-01],\n",
       "       [ 3.79564542e+00,  2.57322973e-01, -5.13767764e-01,\n",
       "        -5.38460965e-02],\n",
       "       [ 1.30079171e+00, -7.61149636e-01, -3.44995038e-01,\n",
       "         4.58247549e-02],\n",
       "       [ 2.42781791e+00,  3.78196013e-01,  2.19119324e-01,\n",
       "        -1.85429264e-01],\n",
       "       [ 1.19900111e+00, -6.06091528e-01,  5.11855509e-01,\n",
       "        -6.09591171e-02],\n",
       "       [ 3.49992004e+00,  4.60674099e-01, -5.73182243e-01,\n",
       "         1.40227954e-01],\n",
       "       [ 1.38876613e+00, -2.04399327e-01, -6.45227566e-02,\n",
       "        -1.63040977e-01],\n",
       "       [ 2.27543050e+00,  3.34990606e-01,  2.86150091e-01,\n",
       "         6.03719696e-02],\n",
       "       [ 2.61409047e+00,  5.60901355e-01, -2.05534524e-01,\n",
       "         2.40704986e-01],\n",
       "       [ 1.25850816e+00, -1.79704795e-01,  4.58477039e-02,\n",
       "        -1.47503846e-01],\n",
       "       [ 1.29113206e+00, -1.16668651e-01,  2.31256463e-01,\n",
       "        -4.02660775e-03],\n",
       "       [ 2.12360872e+00, -2.09729477e-01,  1.54180024e-01,\n",
       "        -5.28273230e-02],\n",
       "       [ 2.38800302e+00,  4.64639805e-01, -4.49530192e-01,\n",
       "         2.31524053e-01],\n",
       "       [ 2.84167278e+00,  3.75269167e-01, -4.98898076e-01,\n",
       "         2.23364626e-02],\n",
       "       [ 3.23067366e+00,  1.37416509e+00, -1.14548205e-01,\n",
       "         2.52901923e-01],\n",
       "       [ 2.15943764e+00, -2.17277579e-01,  2.08763167e-01,\n",
       "        -1.28193066e-01],\n",
       "       [ 1.44416124e+00, -1.43413410e-01, -1.53233888e-01,\n",
       "         1.90996358e-01],\n",
       "       [ 1.78129481e+00, -4.99901681e-01, -1.72875189e-01,\n",
       "         5.05434412e-01],\n",
       "       [ 3.07649993e+00,  6.88085678e-01, -3.35592292e-01,\n",
       "        -3.09828045e-01],\n",
       "       [ 2.14424331e+00,  1.40064201e-01,  7.34878937e-01,\n",
       "        -5.55419691e-02],\n",
       "       [ 1.90509815e+00,  4.93005260e-02,  1.62180236e-01,\n",
       "         2.21202937e-01],\n",
       "       [ 1.16932634e+00, -1.64990262e-01,  2.81835840e-01,\n",
       "        -2.04617872e-02],\n",
       "       [ 2.10761114e+00,  3.72287872e-01,  2.72911321e-02,\n",
       "        -2.10621786e-01],\n",
       "       [ 2.31415471e+00,  1.83651279e-01,  3.22693747e-01,\n",
       "        -2.77653777e-01],\n",
       "       [ 1.92226780e+00,  4.09203467e-01,  1.13586596e-01,\n",
       "        -5.05304967e-01],\n",
       "       [ 1.41523588e+00, -5.74916348e-01,  2.96322527e-01,\n",
       "         1.53046739e-02],\n",
       "       [ 2.56301338e+00,  2.77862603e-01,  2.92569525e-01,\n",
       "        -5.79127477e-02],\n",
       "       [ 2.41874618e+00,  3.04798198e-01,  5.04482664e-01,\n",
       "        -2.41091000e-01],\n",
       "       [ 1.94410979e+00,  1.87532303e-01,  1.77825091e-01,\n",
       "        -4.26195940e-01],\n",
       "       [ 1.52716661e+00, -3.75316983e-01, -1.21898172e-01,\n",
       "        -2.54367442e-01],\n",
       "       [ 1.76434572e+00,  7.88588545e-02,  1.30481631e-01,\n",
       "        -1.37001274e-01],\n",
       "       [ 1.90094161e+00,  1.16627959e-01,  7.23251563e-01,\n",
       "        -4.45953047e-02],\n",
       "       [ 1.39018886e+00, -2.82660938e-01,  3.62909648e-01,\n",
       "         1.55038628e-01]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.22824171, 0.24267075, 0.0782095 , 0.02383509])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92461872, 0.05306648, 0.01710261, 0.00521218])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### acording pca the first colum has a beggiest weightm,that brings the  mind  ,the whole data sets can be represents only with one column,because the firs columns has 90% accurancy,also this mean that the firs column can be represents as linear combination of other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.52834790e-15, -8.55241803e-16, -3.22334751e-16, -6.40598685e-16])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tranformed_data.mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### the mean values of columns is 0 because PCA make substract to evety point mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.04940319, 0.49097143, 0.27872586, 0.1538707 ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tranformed_data.std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the std deviation stands more and more smaller,because the pca orderd columns by its variace the os the purpose of PCA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_atribute = tranformed_data[:,:2]\n",
    "iris_atribute.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_atribute[1].max()\n",
    "iris_label  ### label remain the same ,there is no need to be tranformed with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/daodeiv/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/daodeiv/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=100000.0, class_weight=None, dual=False,\n",
       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
       "          multi_class='warn', n_jobs=None, penalty='l2', random_state=None,\n",
       "          solver='warn', tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ir_normal  = LogisticRegression(C=1e5)\n",
    "ir_normal.fit(iris_data,iris_label)  ### train untraformed data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/daodeiv/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/daodeiv/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "ir_transform =  LogisticRegression(C=1e5).fit(iris_atribute,iris_label) ## train traformed data with PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ir_normal.score(iris_data,iris_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0.98  wich verry good result  ,in original data.We do not expect the score of tranformed data to be more better ,because there are some dimensinaly reduction,be the train time must to be faster "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ir_transform.score(iris_atribute,iris_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0.96 the difference is not so big "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The iris data sets with applied PCA ,shows us the powerful of PCA ,because we loose much data and our score is not so different wich mean ,the does not loose the the siginificat of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "###@ lets to make the but with only one attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 2)\n",
      "(150, 1)\n"
     ]
    }
   ],
   "source": [
    "iris_atribute = tranformed_data[:,:2]\n",
    "\n",
    "print(iris_atribute.shape)\n",
    "iris_atribute = tranformed_data[:,:1]\n",
    "\n",
    "print(iris_atribute.shape)\n",
    "iris_atribute = tranformed_data[:,:1].reshape(,)\n",
    "\n",
    "print(iris_atribute.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/daodeiv/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/daodeiv/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "ir_transform =  LogisticRegression(C=1e5).fit(iris_atribute,iris_label) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ir_transform.score(iris_atribute,iris_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The new score is 94% we lose 4 % but we reduce the data 4 times,the new data is 4 times ,more less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### what we achive with pca \n",
    "[1] the data is more less <br>\n",
    "[2] removed linead dependes values wich does bring sence in our fiitinf<br>\n",
    "[23]COlumns with large values is realiy dominate the other (variace_ration)<br>\n",
    "[removed] corelation columns<br>\n",
    "[removed] the last columns with smalest variance (variance ration)\n",
    "we remove the uncesary noice---THis make a regularazation<br> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "KErnel PCA"
   ]
  }
 ],
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