0005-xgboost-radar.ipynb

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   "source": [
    "import numpy\n",
    "import xgboost\n",
    "import pandas as pd\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "dataset = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/ionosphere/ionosphere.data',header=None)\n",
    "dataset[34] = dataset[34].replace(['g','b'], [1,0])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "X = dataset.drop([34],1)\n",
    "Y = dataset[34]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "seed = 8\n",
    "test_size = 0.2\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = xgboost.XGBClassifier()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 95.77%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  }
 ],
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