ML_LogisticRegression2.ipynb

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    "import graphlab as gl"
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      "This non-commercial license of GraphLab Create for academic use is assigned to padhigayatri.official@gmail.com and will expire on January 02, 2019.\n"
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      "text/html": [
       "<pre>Downloading https://static.turi.com/datasets/xgboost/mushroom.csv to C:/Users/User/AppData/Local/Temp/graphlab-User/3948/3e4e27a5-ed9f-49b7-90a8-0a6c8a91ff4a.csv</pre>"
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       "Downloading https://static.turi.com/datasets/xgboost/mushroom.csv to C:/Users/User/AppData/Local/Temp/graphlab-User/3948/3e4e27a5-ed9f-49b7-90a8-0a6c8a91ff4a.csv"
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       "<pre>Finished parsing file https://static.turi.com/datasets/xgboost/mushroom.csv</pre>"
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       "Finished parsing file https://static.turi.com/datasets/xgboost/mushroom.csv"
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       "Parsing completed. Parsed 100 lines in 0.067003 secs."
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      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[str,str,str,str,str,str,str,str,str,str,str,str,str,str,str,str,str,str,str,str,str,str,str]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
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       "<pre>Finished parsing file https://static.turi.com/datasets/xgboost/mushroom.csv</pre>"
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       "Finished parsing file https://static.turi.com/datasets/xgboost/mushroom.csv"
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   "source": [
    "# The data can be downloaded using\n",
    "data =  gl.SFrame.read_csv('https://static.turi.com/datasets/xgboost/mushroom.csv')\n"
   ]
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   "source": [
    "# Label 'c' is edible\n",
    "data['label'] = data['label'] == 'c'"
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   "source": [
    "# Make a train-test split\n",
    "train_data, test_data = data.random_split(0.8)\n"
   ]
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      "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
      "          You can set ``validation_set=None`` to disable validation tracking.\n",
      "\n"
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       "<pre>WARNING: Detected extremely low variance for feature(s) 'veil-type' because all entries are nearly the same.\n",
       "Proceeding with model training using all features. If the model does not provide results of adequate quality, exclude the above mentioned feature(s) from the input dataset.</pre>"
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       "WARNING: Detected extremely low variance for feature(s) 'veil-type' because all entries are nearly the same.\n",
       "Proceeding with model training using all features. If the model does not provide results of adequate quality, exclude the above mentioned feature(s) from the input dataset."
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     "data": {
      "text/html": [
       "<pre>Logistic regression:</pre>"
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       "Logistic regression:"
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     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
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      "text/plain": [
       "--------------------------------------------------------"
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     "data": {
      "text/html": [
       "<pre>Number of examples          : 6132</pre>"
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       "Number of examples          : 6132"
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      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
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      "text/html": [
       "<pre>Number of feature columns   : 22</pre>"
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      "text/plain": [
       "Number of feature columns   : 22"
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      "text/html": [
       "<pre>Number of unpacked features : 22</pre>"
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      "text/plain": [
       "Number of unpacked features : 22"
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       "<pre>Number of coefficients    : 96</pre>"
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       "Number of coefficients    : 96"
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       "<pre>Starting Newton Method</pre>"
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       "Starting Newton Method"
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      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
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      "text/plain": [
       "--------------------------------------------------------"
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       "<pre>+-----------+----------+--------------+-------------------+---------------------+</pre>"
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       "+-----------+----------+--------------+-------------------+---------------------+"
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      "text/html": [
       "<pre>| Iteration | Passes   | Elapsed Time | Training-accuracy | Validation-accuracy |</pre>"
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       "| Iteration | Passes   | Elapsed Time | Training-accuracy | Validation-accuracy |"
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      "text/html": [
       "<pre>+-----------+----------+--------------+-------------------+---------------------+</pre>"
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      "text/plain": [
       "+-----------+----------+--------------+-------------------+---------------------+"
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       "<pre>| 1         | 2        | 0.016001     | 1.000000          | 1.000000            |</pre>"
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       "| 1         | 2        | 0.016001     | 1.000000          | 1.000000            |"
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       "<pre>| 2         | 3        | 0.031002     | 1.000000          | 1.000000            |</pre>"
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       "| 2         | 3        | 0.031002     | 1.000000          | 1.000000            |"
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       "| 3         | 4        | 0.042003     | 1.000000          | 1.000000            |"
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       "<pre>| 4         | 5        | 0.054003     | 1.000000          | 1.000000            |</pre>"
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       "| 4         | 5        | 0.054003     | 1.000000          | 1.000000            |"
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      "text/html": [
       "<pre>TERMINATED: Iteration limit reached.</pre>"
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      "text/plain": [
       "TERMINATED: Iteration limit reached."
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      "text/html": [
       "<pre>This model may not be optimal. To improve it, consider increasing `max_iterations`.</pre>"
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       "This model may not be optimal. To improve it, consider increasing `max_iterations`."
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   "source": [
    "model = gl.logistic_classifier.create(train_data, target='label',\n",
    "                                      class_weights = 'auto')"
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