diff --git a/model-py/model-xgboost.ipynb b/model-py/model-xgboost.ipynb
index f9056372d8155779a82212e4f1ee980a88b5ff4e..1fd7ca434717bd5607514630ca448d5d43c9a996 100644
--- a/model-py/model-xgboost.ipynb
+++ b/model-py/model-xgboost.ipynb
@@ -9,15 +9,14 @@
"# math and models\n",
"import numpy as np\n",
"import pandas as pd\n",
- "import matplotlib as mpl\n",
- "import matplotlib.pyplot as plt\n",
"import xgboost as xgb\n",
- "import time"
+ "from matplotlib import pyplot\n",
+ "from sklearn.feature_selection import SelectFromModel"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -27,33 +26,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['Subject', 'Gender', 'Age', 'Hemi', 'Group', 'Ecc', 'Pol',\n",
- " 'normEcc', 'normPol', 'Sulc0', 'Curv0', 'Curv2', 'Curv5', 'Curv10',\n",
- " 'PialCurv0', 'PialCurv2', 'PialCurv5', 'PialCurv10', 'Area0',\n",
- " 'Area2', 'Area5', 'Area10', 'MidArea', 'MidArea2', 'MidArea5',\n",
- " 'MidArea10', 'PialArea0', 'PialArea2', 'PialArea5', 'PialArea10',\n",
- " 'LGI0', 'LGI2', 'LGI5', 'LGI10', 'Thick0', 'Thick2', 'Thick5',\n",
- " 'Thick10'], dtype=object)"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.columns.values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -64,1262 +37,3283 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# df_x are your input features\n",
- "x_train = pd.concat([df_train[['Age','normEcc','normPol','Sulc0','Curv5','Area5','LGI5','PialCurv5','PialArea5','MidArea5']]])\n",
- "x_test = pd.concat([df_test[['Age','normEcc','normPol','Sulc0','Curv5','Area5','LGI5','PialCurv5','PialArea5','MidArea5']]])\n",
+ "x_train = pd.concat([df_train[['Age','Ecc','Pol','Sulc0']],df_train.filter(regex='[^Thick1]5')], axis = 1)\n",
+ "x_test = pd.concat([df_test[['Age','Ecc','Pol','Sulc0']],df_test.filter(regex='[^Thick1]5')], axis = 1)\n",
"\n",
"# df_y is your output feature (the one you want to predict)\n",
- "y_train = df_train[['Thick5']]\n",
- "y_test = df_test[['Thick5']]"
+ "y_train = df_train[['Thick0']]\n",
+ "y_test = df_test[['Thick0']]"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n"
- ]
+ "data": {
+ "text/plain": [
+ "Index(['Age', 'Ecc', 'Pol', 'Sulc0', 'Curv5', 'PialCurv5', 'Area5', 'MidArea5',\n",
+ " 'PialArea5', 'LGI5'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "%reset_selective df"
+ "x_test.columns"
]
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Convert input data from numpy to XGBoost format\n",
+ "dtrain = xgb.DMatrix(x_train, y_train)\n",
+ "dtest = xgb.DMatrix(x_test, y_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Testing out Gradient Boost Decision Tree\n",
- "num_round = 1000\n",
+ "num_round = 3000\n",
"\n",
"param = {'tree_method': 'gpu_hist',\n",
" 'max_depth': 6,\n",
- " 'verbosity': 2}\n",
- "\n",
- "# Convert input data from numpy to XGBoost format\n",
- "dtrain = xgb.DMatrix(x_train, y_train)\n",
- "dtest = xgb.DMatrix(x_test, y_test)\n",
+ " 'eta': 0.01,\n",
+ " 'verbosity': 2,\n",
+ " 'eval_metric': ['mae','rmse']}\n",
"\n",
"watchlist = [(dtest,'eval'),(dtrain,'train')]"
]
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[0]\teval-rmse:1.01327\ttrain-rmse:1.00714\n",
- "[1]\teval-rmse:0.745106\ttrain-rmse:0.73975\n",
- "[2]\teval-rmse:0.566756\ttrain-rmse:0.562508\n",
- "[3]\teval-rmse:0.453966\ttrain-rmse:0.450665\n",
- "[4]\teval-rmse:0.382973\ttrain-rmse:0.380653\n",
- "[5]\teval-rmse:0.340953\ttrain-rmse:0.339498\n",
- "[6]\teval-rmse:0.316093\ttrain-rmse:0.315466\n",
- "[7]\teval-rmse:0.301023\ttrain-rmse:0.300895\n",
- "[8]\teval-rmse:0.292458\ttrain-rmse:0.29264\n",
- "[9]\teval-rmse:0.286923\ttrain-rmse:0.287395\n",
- "[10]\teval-rmse:0.284578\ttrain-rmse:0.285057\n",
- "[11]\teval-rmse:0.282054\ttrain-rmse:0.282767\n",
- "[12]\teval-rmse:0.279821\ttrain-rmse:0.280509\n",
- "[13]\teval-rmse:0.278184\ttrain-rmse:0.278962\n",
- "[14]\teval-rmse:0.277913\ttrain-rmse:0.277876\n",
- "[15]\teval-rmse:0.276765\ttrain-rmse:0.276749\n",
- "[16]\teval-rmse:0.275579\ttrain-rmse:0.275568\n",
- "[17]\teval-rmse:0.274645\ttrain-rmse:0.274511\n",
- "[18]\teval-rmse:0.274438\ttrain-rmse:0.274138\n",
- "[19]\teval-rmse:0.274036\ttrain-rmse:0.273642\n",
- "[20]\teval-rmse:0.273518\ttrain-rmse:0.273103\n",
- "[21]\teval-rmse:0.273091\ttrain-rmse:0.272517\n",
- "[22]\teval-rmse:0.272686\ttrain-rmse:0.272103\n",
- "[23]\teval-rmse:0.272199\ttrain-rmse:0.271542\n",
- "[24]\teval-rmse:0.272091\ttrain-rmse:0.271325\n",
- "[25]\teval-rmse:0.272171\ttrain-rmse:0.270806\n",
- "[26]\teval-rmse:0.271768\ttrain-rmse:0.270352\n",
- "[27]\teval-rmse:0.271314\ttrain-rmse:0.269829\n",
- "[28]\teval-rmse:0.270927\ttrain-rmse:0.269342\n",
- "[29]\teval-rmse:0.270746\ttrain-rmse:0.269053\n",
- "[30]\teval-rmse:0.270636\ttrain-rmse:0.268897\n",
- "[31]\teval-rmse:0.270279\ttrain-rmse:0.268535\n",
- "[32]\teval-rmse:0.27003\ttrain-rmse:0.268239\n",
- "[33]\teval-rmse:0.269764\ttrain-rmse:0.267914\n",
- "[34]\teval-rmse:0.26963\ttrain-rmse:0.267729\n",
- "[35]\teval-rmse:0.269432\ttrain-rmse:0.267456\n",
- "[36]\teval-rmse:0.269587\ttrain-rmse:0.267231\n",
- "[37]\teval-rmse:0.269307\ttrain-rmse:0.266969\n",
- "[38]\teval-rmse:0.269157\ttrain-rmse:0.266773\n",
- "[39]\teval-rmse:0.2689\ttrain-rmse:0.266464\n",
- "[40]\teval-rmse:0.268712\ttrain-rmse:0.266282\n",
- "[41]\teval-rmse:0.268661\ttrain-rmse:0.266154\n",
- "[42]\teval-rmse:0.268783\ttrain-rmse:0.265886\n",
- "[43]\teval-rmse:0.268553\ttrain-rmse:0.265607\n",
- "[44]\teval-rmse:0.268488\ttrain-rmse:0.265515\n",
- "[45]\teval-rmse:0.268485\ttrain-rmse:0.265449\n",
- "[46]\teval-rmse:0.268242\ttrain-rmse:0.265134\n",
- "[47]\teval-rmse:0.268061\ttrain-rmse:0.264944\n",
- "[48]\teval-rmse:0.267987\ttrain-rmse:0.264821\n",
- "[49]\teval-rmse:0.26759\ttrain-rmse:0.264413\n",
- "[50]\teval-rmse:0.267415\ttrain-rmse:0.264147\n",
- "[51]\teval-rmse:0.267253\ttrain-rmse:0.263958\n",
- "[52]\teval-rmse:0.267135\ttrain-rmse:0.263818\n",
- "[53]\teval-rmse:0.26705\ttrain-rmse:0.263639\n",
- "[54]\teval-rmse:0.266993\ttrain-rmse:0.263553\n",
- "[55]\teval-rmse:0.266956\ttrain-rmse:0.263446\n",
- "[56]\teval-rmse:0.266837\ttrain-rmse:0.263299\n",
- "[57]\teval-rmse:0.266715\ttrain-rmse:0.263159\n",
- "[58]\teval-rmse:0.266622\ttrain-rmse:0.262985\n",
- "[59]\teval-rmse:0.266469\ttrain-rmse:0.262816\n",
- "[60]\teval-rmse:0.266587\ttrain-rmse:0.262655\n",
- "[61]\teval-rmse:0.266551\ttrain-rmse:0.26254\n",
- "[62]\teval-rmse:0.266479\ttrain-rmse:0.26246\n",
- "[63]\teval-rmse:0.266437\ttrain-rmse:0.262376\n",
- "[64]\teval-rmse:0.266403\ttrain-rmse:0.262323\n",
- "[65]\teval-rmse:0.266222\ttrain-rmse:0.262082\n",
- "[66]\teval-rmse:0.266174\ttrain-rmse:0.261965\n",
- "[67]\teval-rmse:0.266079\ttrain-rmse:0.261788\n",
- "[68]\teval-rmse:0.266073\ttrain-rmse:0.261744\n",
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- "[75]\teval-rmse:0.265627\ttrain-rmse:0.260811\n",
- "[76]\teval-rmse:0.265506\ttrain-rmse:0.260666\n",
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- "[78]\teval-rmse:0.265255\ttrain-rmse:0.260347\n",
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- "[90]\teval-rmse:0.264796\ttrain-rmse:0.259335\n",
- "[91]\teval-rmse:0.264769\ttrain-rmse:0.259261\n",
- "[92]\teval-rmse:0.264711\ttrain-rmse:0.25917\n",
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- "[95]\teval-rmse:0.264631\ttrain-rmse:0.258972\n",
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- "[108]\teval-rmse:0.264286\ttrain-rmse:0.257957\n",
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- "[121]\teval-rmse:0.263996\ttrain-rmse:0.256965\n",
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- "[127]\teval-rmse:0.263826\ttrain-rmse:0.256416\n",
- "[128]\teval-rmse:0.263824\ttrain-rmse:0.256353\n",
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- "[130]\teval-rmse:0.263802\ttrain-rmse:0.25628\n",
- "[131]\teval-rmse:0.263777\ttrain-rmse:0.256224\n",
- "[132]\teval-rmse:0.263759\ttrain-rmse:0.256177\n",
- "[133]\teval-rmse:0.263756\ttrain-rmse:0.256156\n",
- "[134]\teval-rmse:0.263771\ttrain-rmse:0.256108\n",
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- "[143]\teval-rmse:0.263692\ttrain-rmse:0.255662\n",
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- "[185]\teval-rmse:0.263239\ttrain-rmse:0.253425\n"
+ "[0]\teval-mae:1.33779\teval-rmse:1.44401\ttrain-mae:1.33084\ttrain-rmse:1.43634\n",
+ "[1]\teval-mae:1.32446\teval-rmse:1.43111\ttrain-mae:1.31753\ttrain-rmse:1.42347\n",
+ "[2]\teval-mae:1.31127\teval-rmse:1.41836\ttrain-mae:1.30435\ttrain-rmse:1.41075\n",
+ "[3]\teval-mae:1.29821\teval-rmse:1.40575\ttrain-mae:1.29131\ttrain-rmse:1.39816\n",
+ "[4]\teval-mae:1.28528\teval-rmse:1.39328\ttrain-mae:1.2784\ttrain-rmse:1.38573\n",
+ "[5]\teval-mae:1.27248\teval-rmse:1.38094\ttrain-mae:1.26561\ttrain-rmse:1.37342\n",
+ "[6]\teval-mae:1.25981\teval-rmse:1.36875\ttrain-mae:1.25296\ttrain-rmse:1.36126\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
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},
{
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]
},
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
- "data": {
- "text/plain": [
- "<xgboost.core.Booster at 0x2aaaffb5b0f0>"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
+ "name": "stdout",
+ "output_type": "stream",
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+ "output_type": "stream",
+ "text": [
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+ "[1897]\teval-mae:0.314676\teval-rmse:0.401925\ttrain-mae:0.309951\ttrain-rmse:0.395502\n",
+ "[1898]\teval-mae:0.314676\teval-rmse:0.401925\ttrain-mae:0.309947\ttrain-rmse:0.395496\n",
+ "[1899]\teval-mae:0.314675\teval-rmse:0.401924\ttrain-mae:0.309944\ttrain-rmse:0.395493\n",
+ "[1900]\teval-mae:0.314673\teval-rmse:0.401922\ttrain-mae:0.309941\ttrain-rmse:0.395489\n",
+ "[1901]\teval-mae:0.314667\teval-rmse:0.401917\ttrain-mae:0.309934\ttrain-rmse:0.395483\n",
+ "[1902]\teval-mae:0.314664\teval-rmse:0.401914\ttrain-mae:0.30993\ttrain-rmse:0.395479\n",
+ "[1903]\teval-mae:0.314658\teval-rmse:0.401907\ttrain-mae:0.309923\ttrain-rmse:0.395471\n",
+ "[1904]\teval-mae:0.314656\teval-rmse:0.401904\ttrain-mae:0.30992\ttrain-rmse:0.395466\n",
+ "[1905]\teval-mae:0.314652\teval-rmse:0.4019\ttrain-mae:0.309915\ttrain-rmse:0.395461\n",
+ "[1906]\teval-mae:0.314646\teval-rmse:0.401892\ttrain-mae:0.309909\ttrain-rmse:0.395454\n",
+ "[1907]\teval-mae:0.314644\teval-rmse:0.40189\ttrain-mae:0.309906\ttrain-rmse:0.39545\n",
+ "[1908]\teval-mae:0.314636\teval-rmse:0.401883\ttrain-mae:0.309897\ttrain-rmse:0.39544\n",
+ "[1909]\teval-mae:0.314635\teval-rmse:0.401882\ttrain-mae:0.309894\ttrain-rmse:0.395436\n",
+ "[1910]\teval-mae:0.314632\teval-rmse:0.401879\ttrain-mae:0.30989\ttrain-rmse:0.395431\n",
+ "[1911]\teval-mae:0.31463\teval-rmse:0.401877\ttrain-mae:0.309886\ttrain-rmse:0.395427\n",
+ "[1912]\teval-mae:0.314627\teval-rmse:0.401873\ttrain-mae:0.309879\ttrain-rmse:0.395418\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1913]\teval-mae:0.314624\teval-rmse:0.401871\ttrain-mae:0.309876\ttrain-rmse:0.395415\n",
+ "[1914]\teval-mae:0.314624\teval-rmse:0.40187\ttrain-mae:0.309874\ttrain-rmse:0.395413\n",
+ "[1915]\teval-mae:0.314623\teval-rmse:0.401869\ttrain-mae:0.309872\ttrain-rmse:0.39541\n",
+ "[1916]\teval-mae:0.314618\teval-rmse:0.401863\ttrain-mae:0.309866\ttrain-rmse:0.395403\n",
+ "[1917]\teval-mae:0.314616\teval-rmse:0.401862\ttrain-mae:0.309863\ttrain-rmse:0.3954\n",
+ "[1918]\teval-mae:0.314615\teval-rmse:0.40186\ttrain-mae:0.309861\ttrain-rmse:0.395398\n",
+ "[1919]\teval-mae:0.314614\teval-rmse:0.40186\ttrain-mae:0.309858\ttrain-rmse:0.395394\n",
+ "[1920]\teval-mae:0.314614\teval-rmse:0.401859\ttrain-mae:0.309855\ttrain-rmse:0.395391\n",
+ "[1921]\teval-mae:0.314607\teval-rmse:0.401852\ttrain-mae:0.309848\ttrain-rmse:0.395382\n",
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+ "[1923]\teval-mae:0.314601\teval-rmse:0.401845\ttrain-mae:0.309836\ttrain-rmse:0.395368\n",
+ "[1924]\teval-mae:0.3146\teval-rmse:0.401844\ttrain-mae:0.309834\ttrain-rmse:0.395365\n",
+ "[1925]\teval-mae:0.314596\teval-rmse:0.401839\ttrain-mae:0.309829\ttrain-rmse:0.395359\n",
+ "[1926]\teval-mae:0.314591\teval-rmse:0.401832\ttrain-mae:0.309823\ttrain-rmse:0.395351\n",
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+ "[1929]\teval-mae:0.314581\teval-rmse:0.401821\ttrain-mae:0.309811\ttrain-rmse:0.395336\n",
+ "[1930]\teval-mae:0.314578\teval-rmse:0.401817\ttrain-mae:0.309804\ttrain-rmse:0.395327\n",
+ "[1931]\teval-mae:0.314577\teval-rmse:0.401816\ttrain-mae:0.309802\ttrain-rmse:0.395325\n",
+ "[1932]\teval-mae:0.314574\teval-rmse:0.401813\ttrain-mae:0.309798\ttrain-rmse:0.395321\n",
+ "[1933]\teval-mae:0.314573\teval-rmse:0.401812\ttrain-mae:0.309796\ttrain-rmse:0.395319\n",
+ "[1934]\teval-mae:0.314572\teval-rmse:0.401811\ttrain-mae:0.309793\ttrain-rmse:0.395315\n",
+ "[1935]\teval-mae:0.314571\teval-rmse:0.40181\ttrain-mae:0.309792\ttrain-rmse:0.395314\n",
+ "[1936]\teval-mae:0.314568\teval-rmse:0.401807\ttrain-mae:0.309785\ttrain-rmse:0.395305\n",
+ "[1937]\teval-mae:0.314567\teval-rmse:0.401805\ttrain-mae:0.309783\ttrain-rmse:0.395303\n",
+ "[1938]\teval-mae:0.31456\teval-rmse:0.401798\ttrain-mae:0.309775\ttrain-rmse:0.395294\n",
+ "[1939]\teval-mae:0.314557\teval-rmse:0.401795\ttrain-mae:0.309772\ttrain-rmse:0.395291\n",
+ "[1940]\teval-mae:0.314555\teval-rmse:0.401793\ttrain-mae:0.309769\ttrain-rmse:0.395288\n",
+ "[1941]\teval-mae:0.314554\teval-rmse:0.401792\ttrain-mae:0.309766\ttrain-rmse:0.395284\n",
+ "[1942]\teval-mae:0.314552\teval-rmse:0.40179\ttrain-mae:0.309763\ttrain-rmse:0.39528\n",
+ "[1943]\teval-mae:0.314553\teval-rmse:0.401791\ttrain-mae:0.30976\ttrain-rmse:0.395277\n",
+ "[1944]\teval-mae:0.314547\teval-rmse:0.401784\ttrain-mae:0.309753\ttrain-rmse:0.395268\n",
+ "[1945]\teval-mae:0.314546\teval-rmse:0.401783\ttrain-mae:0.30975\ttrain-rmse:0.395265\n",
+ "[1946]\teval-mae:0.314541\teval-rmse:0.401777\ttrain-mae:0.309744\ttrain-rmse:0.395258\n",
+ "[1947]\teval-mae:0.314536\teval-rmse:0.401773\ttrain-mae:0.309738\ttrain-rmse:0.395251\n",
+ "[1948]\teval-mae:0.314536\teval-rmse:0.401773\ttrain-mae:0.309733\ttrain-rmse:0.395245\n",
+ "[1949]\teval-mae:0.314534\teval-rmse:0.401771\ttrain-mae:0.30973\ttrain-rmse:0.395241\n",
+ "[1950]\teval-mae:0.314532\teval-rmse:0.40177\ttrain-mae:0.309727\ttrain-rmse:0.395238\n",
+ "[1951]\teval-mae:0.314528\teval-rmse:0.401765\ttrain-mae:0.30972\ttrain-rmse:0.395232\n",
+ "[1952]\teval-mae:0.314526\teval-rmse:0.401762\ttrain-mae:0.309718\ttrain-rmse:0.395228\n",
+ "[1953]\teval-mae:0.314524\teval-rmse:0.401761\ttrain-mae:0.309715\ttrain-rmse:0.395225\n",
+ "[1954]\teval-mae:0.314519\teval-rmse:0.401756\ttrain-mae:0.309708\ttrain-rmse:0.395217\n",
+ "[1955]\teval-mae:0.314518\teval-rmse:0.401754\ttrain-mae:0.309705\ttrain-rmse:0.395214\n",
+ "[1956]\teval-mae:0.314514\teval-rmse:0.40175\ttrain-mae:0.3097\ttrain-rmse:0.395208\n",
+ "[1957]\teval-mae:0.314515\teval-rmse:0.401751\ttrain-mae:0.309697\ttrain-rmse:0.395204\n",
+ "[1958]\teval-mae:0.314514\teval-rmse:0.40175\ttrain-mae:0.309694\ttrain-rmse:0.395201\n",
+ "[1959]\teval-mae:0.314512\teval-rmse:0.401749\ttrain-mae:0.309692\ttrain-rmse:0.395198\n",
+ "[1960]\teval-mae:0.31451\teval-rmse:0.401746\ttrain-mae:0.309688\ttrain-rmse:0.395195\n",
+ "[1961]\teval-mae:0.314508\teval-rmse:0.401745\ttrain-mae:0.309684\ttrain-rmse:0.395191\n",
+ "[1962]\teval-mae:0.314503\teval-rmse:0.401739\ttrain-mae:0.309677\ttrain-rmse:0.395182\n",
+ "[1963]\teval-mae:0.314498\teval-rmse:0.401734\ttrain-mae:0.309671\ttrain-rmse:0.395176\n",
+ "[1964]\teval-mae:0.314493\teval-rmse:0.40173\ttrain-mae:0.309664\ttrain-rmse:0.395167\n",
+ "[1965]\teval-mae:0.314492\teval-rmse:0.401729\ttrain-mae:0.309663\ttrain-rmse:0.395166\n",
+ "[1966]\teval-mae:0.31449\teval-rmse:0.401726\ttrain-mae:0.309656\ttrain-rmse:0.395157\n",
+ "[1967]\teval-mae:0.314488\teval-rmse:0.401724\ttrain-mae:0.309653\ttrain-rmse:0.395153\n",
+ "[1968]\teval-mae:0.314486\teval-rmse:0.401723\ttrain-mae:0.309651\ttrain-rmse:0.395151\n",
+ "[1969]\teval-mae:0.314485\teval-rmse:0.401722\ttrain-mae:0.309648\ttrain-rmse:0.395148\n",
+ "[1970]\teval-mae:0.314483\teval-rmse:0.401719\ttrain-mae:0.309642\ttrain-rmse:0.395139\n",
+ "[1971]\teval-mae:0.31448\teval-rmse:0.401717\ttrain-mae:0.309638\ttrain-rmse:0.395136\n",
+ "[1972]\teval-mae:0.314475\teval-rmse:0.40171\ttrain-mae:0.309631\ttrain-rmse:0.395128\n",
+ "[1973]\teval-mae:0.314473\teval-rmse:0.401709\ttrain-mae:0.309628\ttrain-rmse:0.395125\n",
+ "[1974]\teval-mae:0.314472\teval-rmse:0.401708\ttrain-mae:0.309625\ttrain-rmse:0.395122\n",
+ "[1975]\teval-mae:0.31447\teval-rmse:0.401706\ttrain-mae:0.309623\ttrain-rmse:0.395119\n",
+ "[1976]\teval-mae:0.314466\teval-rmse:0.401701\ttrain-mae:0.309618\ttrain-rmse:0.395112\n",
+ "[1977]\teval-mae:0.314465\teval-rmse:0.401701\ttrain-mae:0.309616\ttrain-rmse:0.39511\n",
+ "[1978]\teval-mae:0.314463\teval-rmse:0.401698\ttrain-mae:0.309612\ttrain-rmse:0.395107\n",
+ "[1979]\teval-mae:0.314456\teval-rmse:0.40169\ttrain-mae:0.309604\ttrain-rmse:0.395097\n",
+ "[1980]\teval-mae:0.314454\teval-rmse:0.401687\ttrain-mae:0.309598\ttrain-rmse:0.395088\n",
+ "[1981]\teval-mae:0.314448\teval-rmse:0.401682\ttrain-mae:0.309591\ttrain-rmse:0.395082\n",
+ "[1982]\teval-mae:0.314447\teval-rmse:0.401681\ttrain-mae:0.309588\ttrain-rmse:0.395079\n",
+ "[1983]\teval-mae:0.314446\teval-rmse:0.40168\ttrain-mae:0.309586\ttrain-rmse:0.395076\n",
+ "[1984]\teval-mae:0.314442\teval-rmse:0.401676\ttrain-mae:0.309581\ttrain-rmse:0.39507\n",
+ "[1985]\teval-mae:0.314441\teval-rmse:0.401674\ttrain-mae:0.309579\ttrain-rmse:0.395067\n",
+ "[1986]\teval-mae:0.314439\teval-rmse:0.401672\ttrain-mae:0.309576\ttrain-rmse:0.395064\n",
+ "[1987]\teval-mae:0.314438\teval-rmse:0.401672\ttrain-mae:0.309574\ttrain-rmse:0.395063\n",
+ "[1988]\teval-mae:0.314436\teval-rmse:0.401669\ttrain-mae:0.309568\ttrain-rmse:0.395054\n",
+ "[1989]\teval-mae:0.314437\teval-rmse:0.40167\ttrain-mae:0.309565\ttrain-rmse:0.395051\n",
+ "[1990]\teval-mae:0.314435\teval-rmse:0.401668\ttrain-mae:0.309562\ttrain-rmse:0.395047\n",
+ "[1991]\teval-mae:0.31443\teval-rmse:0.401662\ttrain-mae:0.309556\ttrain-rmse:0.395041\n",
+ "[1992]\teval-mae:0.314424\teval-rmse:0.401656\ttrain-mae:0.30955\ttrain-rmse:0.395033\n",
+ "[1993]\teval-mae:0.314423\teval-rmse:0.401655\ttrain-mae:0.309547\ttrain-rmse:0.39503\n",
+ "[1994]\teval-mae:0.314417\teval-rmse:0.401647\ttrain-mae:0.309539\ttrain-rmse:0.39502\n",
+ "[1995]\teval-mae:0.314413\teval-rmse:0.401643\ttrain-mae:0.309535\ttrain-rmse:0.395015\n",
+ "[1996]\teval-mae:0.314412\teval-rmse:0.401642\ttrain-mae:0.309533\ttrain-rmse:0.395013\n",
+ "[1997]\teval-mae:0.31441\teval-rmse:0.401641\ttrain-mae:0.30953\ttrain-rmse:0.39501\n",
+ "[1998]\teval-mae:0.314406\teval-rmse:0.401635\ttrain-mae:0.309524\ttrain-rmse:0.395003\n",
+ "[1999]\teval-mae:0.314404\teval-rmse:0.401634\ttrain-mae:0.309522\ttrain-rmse:0.395\n",
+ "[2000]\teval-mae:0.314404\teval-rmse:0.401633\ttrain-mae:0.309519\ttrain-rmse:0.394997\n",
+ "[2001]\teval-mae:0.314401\teval-rmse:0.401631\ttrain-mae:0.309516\ttrain-rmse:0.394994\n",
+ "[2002]\teval-mae:0.314396\teval-rmse:0.401625\ttrain-mae:0.30951\ttrain-rmse:0.394988\n",
+ "[2003]\teval-mae:0.314396\teval-rmse:0.401626\ttrain-mae:0.309508\ttrain-rmse:0.394986\n",
+ "[2004]\teval-mae:0.314397\teval-rmse:0.401627\ttrain-mae:0.309505\ttrain-rmse:0.394982\n",
+ "[2005]\teval-mae:0.314395\teval-rmse:0.401624\ttrain-mae:0.309502\ttrain-rmse:0.394978\n",
+ "[2006]\teval-mae:0.314394\teval-rmse:0.401624\ttrain-mae:0.309501\ttrain-rmse:0.394977\n",
+ "[2007]\teval-mae:0.314392\teval-rmse:0.401622\ttrain-mae:0.309496\ttrain-rmse:0.394972\n",
+ "[2008]\teval-mae:0.314387\teval-rmse:0.401617\ttrain-mae:0.309489\ttrain-rmse:0.394964\n",
+ "[2009]\teval-mae:0.314385\teval-rmse:0.401615\ttrain-mae:0.309486\ttrain-rmse:0.39496\n",
+ "[2010]\teval-mae:0.314382\teval-rmse:0.401611\ttrain-mae:0.309481\ttrain-rmse:0.394954\n",
+ "[2011]\teval-mae:0.314376\teval-rmse:0.401605\ttrain-mae:0.309474\ttrain-rmse:0.394946\n",
+ "[2012]\teval-mae:0.314374\teval-rmse:0.401603\ttrain-mae:0.309472\ttrain-rmse:0.394943\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2013]\teval-mae:0.314374\teval-rmse:0.401604\ttrain-mae:0.309469\ttrain-rmse:0.39494\n",
+ "[2014]\teval-mae:0.314372\teval-rmse:0.401602\ttrain-mae:0.309467\ttrain-rmse:0.394938\n",
+ "[2015]\teval-mae:0.314366\teval-rmse:0.401595\ttrain-mae:0.309459\ttrain-rmse:0.394928\n",
+ "[2016]\teval-mae:0.314357\teval-rmse:0.401585\ttrain-mae:0.309449\ttrain-rmse:0.394916\n",
+ "[2017]\teval-mae:0.314354\teval-rmse:0.401582\ttrain-mae:0.309444\ttrain-rmse:0.394911\n",
+ "[2018]\teval-mae:0.314351\teval-rmse:0.401579\ttrain-mae:0.30944\ttrain-rmse:0.394906\n",
+ "[2019]\teval-mae:0.314351\teval-rmse:0.401579\ttrain-mae:0.309435\ttrain-rmse:0.394901\n",
+ "[2020]\teval-mae:0.31435\teval-rmse:0.401579\ttrain-mae:0.309433\ttrain-rmse:0.394898\n",
+ "[2021]\teval-mae:0.314349\teval-rmse:0.401577\ttrain-mae:0.309429\ttrain-rmse:0.394893\n",
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+ "[2026]\teval-mae:0.314339\teval-rmse:0.401567\ttrain-mae:0.309415\ttrain-rmse:0.394877\n",
+ "[2027]\teval-mae:0.314337\teval-rmse:0.401564\ttrain-mae:0.309408\ttrain-rmse:0.394868\n",
+ "[2028]\teval-mae:0.314336\teval-rmse:0.401563\ttrain-mae:0.309405\ttrain-rmse:0.394865\n",
+ "[2029]\teval-mae:0.314331\teval-rmse:0.401558\ttrain-mae:0.309399\ttrain-rmse:0.394859\n",
+ "[2030]\teval-mae:0.314326\teval-rmse:0.401552\ttrain-mae:0.309393\ttrain-rmse:0.394851\n",
+ "[2031]\teval-mae:0.314325\teval-rmse:0.401551\ttrain-mae:0.30939\ttrain-rmse:0.394848\n",
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+ "[2034]\teval-mae:0.314312\teval-rmse:0.401536\ttrain-mae:0.309376\ttrain-rmse:0.394831\n",
+ "[2035]\teval-mae:0.314311\teval-rmse:0.401535\ttrain-mae:0.309373\ttrain-rmse:0.394828\n",
+ "[2036]\teval-mae:0.314305\teval-rmse:0.401529\ttrain-mae:0.309366\ttrain-rmse:0.39482\n",
+ "[2037]\teval-mae:0.314304\teval-rmse:0.401528\ttrain-mae:0.309364\ttrain-rmse:0.394818\n",
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+ "[2039]\teval-mae:0.314299\teval-rmse:0.401523\ttrain-mae:0.309357\ttrain-rmse:0.394809\n",
+ "[2040]\teval-mae:0.314298\teval-rmse:0.401523\ttrain-mae:0.309354\ttrain-rmse:0.394806\n",
+ "[2041]\teval-mae:0.314294\teval-rmse:0.401517\ttrain-mae:0.309348\ttrain-rmse:0.3948\n",
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+ ]
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ "[2917]\teval-mae:0.312623\teval-rmse:0.399731\ttrain-mae:0.30653\ttrain-rmse:0.391477\n",
+ "[2918]\teval-mae:0.312621\teval-rmse:0.399728\ttrain-mae:0.306527\ttrain-rmse:0.391474\n",
+ "[2919]\teval-mae:0.31262\teval-rmse:0.399727\ttrain-mae:0.306525\ttrain-rmse:0.391471\n",
+ "[2920]\teval-mae:0.312617\teval-rmse:0.399725\ttrain-mae:0.306521\ttrain-rmse:0.391467\n",
+ "[2921]\teval-mae:0.312617\teval-rmse:0.399725\ttrain-mae:0.30652\ttrain-rmse:0.391465\n",
+ "[2922]\teval-mae:0.312617\teval-rmse:0.399725\ttrain-mae:0.306519\ttrain-rmse:0.391464\n",
+ "[2923]\teval-mae:0.312616\teval-rmse:0.399724\ttrain-mae:0.306517\ttrain-rmse:0.391463\n",
+ "[2924]\teval-mae:0.312616\teval-rmse:0.399724\ttrain-mae:0.306516\ttrain-rmse:0.39146\n",
+ "[2925]\teval-mae:0.312616\teval-rmse:0.399724\ttrain-mae:0.306515\ttrain-rmse:0.391459\n",
+ "[2926]\teval-mae:0.312616\teval-rmse:0.399724\ttrain-mae:0.306511\ttrain-rmse:0.391454\n",
+ "[2927]\teval-mae:0.312614\teval-rmse:0.399722\ttrain-mae:0.306509\ttrain-rmse:0.391451\n",
+ "[2928]\teval-mae:0.312614\teval-rmse:0.399722\ttrain-mae:0.306508\ttrain-rmse:0.39145\n",
+ "[2929]\teval-mae:0.312614\teval-rmse:0.399721\ttrain-mae:0.306506\ttrain-rmse:0.391449\n",
+ "[2930]\teval-mae:0.312613\teval-rmse:0.399721\ttrain-mae:0.306503\ttrain-rmse:0.391444\n",
+ "[2931]\teval-mae:0.312612\teval-rmse:0.39972\ttrain-mae:0.3065\ttrain-rmse:0.391441\n",
+ "[2932]\teval-mae:0.312611\teval-rmse:0.399719\ttrain-mae:0.306499\ttrain-rmse:0.39144\n",
+ "[2933]\teval-mae:0.312611\teval-rmse:0.399719\ttrain-mae:0.306498\ttrain-rmse:0.391439\n",
+ "[2934]\teval-mae:0.312611\teval-rmse:0.399719\ttrain-mae:0.306496\ttrain-rmse:0.391436\n",
+ "[2935]\teval-mae:0.31261\teval-rmse:0.399718\ttrain-mae:0.306494\ttrain-rmse:0.391434\n",
+ "[2936]\teval-mae:0.312608\teval-rmse:0.399716\ttrain-mae:0.30649\ttrain-rmse:0.39143\n",
+ "[2937]\teval-mae:0.312607\teval-rmse:0.399715\ttrain-mae:0.306488\ttrain-rmse:0.391427\n",
+ "[2938]\teval-mae:0.312605\teval-rmse:0.399712\ttrain-mae:0.306485\ttrain-rmse:0.391424\n",
+ "[2939]\teval-mae:0.312603\teval-rmse:0.399711\ttrain-mae:0.306483\ttrain-rmse:0.391422\n",
+ "[2940]\teval-mae:0.312602\teval-rmse:0.39971\ttrain-mae:0.306482\ttrain-rmse:0.39142\n",
+ "[2941]\teval-mae:0.312602\teval-rmse:0.39971\ttrain-mae:0.30648\ttrain-rmse:0.391418\n",
+ "[2942]\teval-mae:0.312602\teval-rmse:0.39971\ttrain-mae:0.306479\ttrain-rmse:0.391417\n",
+ "[2943]\teval-mae:0.312601\teval-rmse:0.39971\ttrain-mae:0.306477\ttrain-rmse:0.391415\n",
+ "[2944]\teval-mae:0.3126\teval-rmse:0.399709\ttrain-mae:0.306474\ttrain-rmse:0.391412\n",
+ "[2945]\teval-mae:0.312598\teval-rmse:0.399706\ttrain-mae:0.306471\ttrain-rmse:0.391408\n",
+ "[2946]\teval-mae:0.312597\teval-rmse:0.399706\ttrain-mae:0.306469\ttrain-rmse:0.391406\n",
+ "[2947]\teval-mae:0.312596\teval-rmse:0.399705\ttrain-mae:0.306467\ttrain-rmse:0.391403\n",
+ "[2948]\teval-mae:0.312596\teval-rmse:0.399705\ttrain-mae:0.306466\ttrain-rmse:0.391402\n",
+ "[2949]\teval-mae:0.312595\teval-rmse:0.399704\ttrain-mae:0.306464\ttrain-rmse:0.391401\n",
+ "[2950]\teval-mae:0.312595\teval-rmse:0.399704\ttrain-mae:0.306463\ttrain-rmse:0.391399\n",
+ "[2951]\teval-mae:0.312594\teval-rmse:0.399703\ttrain-mae:0.306461\ttrain-rmse:0.391397\n",
+ "[2952]\teval-mae:0.312593\teval-rmse:0.399702\ttrain-mae:0.306459\ttrain-rmse:0.391393\n",
+ "[2953]\teval-mae:0.312592\teval-rmse:0.399701\ttrain-mae:0.306456\ttrain-rmse:0.391391\n",
+ "[2954]\teval-mae:0.31259\teval-rmse:0.399698\ttrain-mae:0.306452\ttrain-rmse:0.391386\n",
+ "[2955]\teval-mae:0.312585\teval-rmse:0.399693\ttrain-mae:0.306447\ttrain-rmse:0.39138\n",
+ "[2956]\teval-mae:0.312584\teval-rmse:0.399691\ttrain-mae:0.306444\ttrain-rmse:0.391376\n",
+ "[2957]\teval-mae:0.312583\teval-rmse:0.399691\ttrain-mae:0.306443\ttrain-rmse:0.391375\n",
+ "[2958]\teval-mae:0.312583\teval-rmse:0.399691\ttrain-mae:0.306441\ttrain-rmse:0.391373\n",
+ "[2959]\teval-mae:0.312581\teval-rmse:0.399688\ttrain-mae:0.306438\ttrain-rmse:0.391369\n",
+ "[2960]\teval-mae:0.312579\teval-rmse:0.399686\ttrain-mae:0.306435\ttrain-rmse:0.391365\n",
+ "[2961]\teval-mae:0.312578\teval-rmse:0.399686\ttrain-mae:0.306433\ttrain-rmse:0.391363\n",
+ "[2962]\teval-mae:0.312577\teval-rmse:0.399685\ttrain-mae:0.306431\ttrain-rmse:0.391361\n",
+ "[2963]\teval-mae:0.312574\teval-rmse:0.399681\ttrain-mae:0.306427\ttrain-rmse:0.391357\n",
+ "[2964]\teval-mae:0.312573\teval-rmse:0.399681\ttrain-mae:0.306426\ttrain-rmse:0.391355\n",
+ "[2965]\teval-mae:0.312572\teval-rmse:0.39968\ttrain-mae:0.306424\ttrain-rmse:0.391353\n",
+ "[2966]\teval-mae:0.312573\teval-rmse:0.39968\ttrain-mae:0.30642\ttrain-rmse:0.391348\n",
+ "[2967]\teval-mae:0.312573\teval-rmse:0.39968\ttrain-mae:0.306419\ttrain-rmse:0.391346\n",
+ "[2968]\teval-mae:0.312573\teval-rmse:0.39968\ttrain-mae:0.306417\ttrain-rmse:0.391345\n",
+ "[2969]\teval-mae:0.312572\teval-rmse:0.399679\ttrain-mae:0.306415\ttrain-rmse:0.391342\n",
+ "[2970]\teval-mae:0.31257\teval-rmse:0.399677\ttrain-mae:0.306412\ttrain-rmse:0.391339\n",
+ "[2971]\teval-mae:0.312569\teval-rmse:0.399676\ttrain-mae:0.30641\ttrain-rmse:0.391336\n",
+ "[2972]\teval-mae:0.312568\teval-rmse:0.399676\ttrain-mae:0.306409\ttrain-rmse:0.391334\n",
+ "[2973]\teval-mae:0.312566\teval-rmse:0.399674\ttrain-mae:0.306405\ttrain-rmse:0.39133\n",
+ "[2974]\teval-mae:0.312566\teval-rmse:0.399675\ttrain-mae:0.306402\ttrain-rmse:0.391326\n",
+ "[2975]\teval-mae:0.312566\teval-rmse:0.399675\ttrain-mae:0.3064\ttrain-rmse:0.391324\n",
+ "[2976]\teval-mae:0.312566\teval-rmse:0.399675\ttrain-mae:0.306398\ttrain-rmse:0.391322\n",
+ "[2977]\teval-mae:0.312566\teval-rmse:0.399675\ttrain-mae:0.306397\ttrain-rmse:0.39132\n",
+ "[2978]\teval-mae:0.312561\teval-rmse:0.39967\ttrain-mae:0.306392\ttrain-rmse:0.391314\n",
+ "[2979]\teval-mae:0.312561\teval-rmse:0.399669\ttrain-mae:0.30639\ttrain-rmse:0.391312\n",
+ "[2980]\teval-mae:0.31256\teval-rmse:0.399669\ttrain-mae:0.306387\ttrain-rmse:0.391308\n",
+ "[2981]\teval-mae:0.312559\teval-rmse:0.399668\ttrain-mae:0.306385\ttrain-rmse:0.391306\n",
+ "[2982]\teval-mae:0.312557\teval-rmse:0.399666\ttrain-mae:0.306382\ttrain-rmse:0.391302\n",
+ "[2983]\teval-mae:0.312555\teval-rmse:0.399663\ttrain-mae:0.306379\ttrain-rmse:0.391298\n",
+ "[2984]\teval-mae:0.312555\teval-rmse:0.399663\ttrain-mae:0.306377\ttrain-rmse:0.391295\n",
+ "[2985]\teval-mae:0.312554\teval-rmse:0.399662\ttrain-mae:0.306376\ttrain-rmse:0.391294\n",
+ "[2986]\teval-mae:0.312554\teval-rmse:0.399662\ttrain-mae:0.306372\ttrain-rmse:0.391289\n",
+ "[2987]\teval-mae:0.312554\teval-rmse:0.399662\ttrain-mae:0.306371\ttrain-rmse:0.391288\n",
+ "[2988]\teval-mae:0.312551\teval-rmse:0.399659\ttrain-mae:0.306368\ttrain-rmse:0.391284\n",
+ "[2989]\teval-mae:0.312551\teval-rmse:0.399659\ttrain-mae:0.306367\ttrain-rmse:0.391283\n",
+ "[2990]\teval-mae:0.31255\teval-rmse:0.399658\ttrain-mae:0.306365\ttrain-rmse:0.391281\n",
+ "[2991]\teval-mae:0.31255\teval-rmse:0.399658\ttrain-mae:0.306363\ttrain-rmse:0.391279\n",
+ "[2992]\teval-mae:0.312547\teval-rmse:0.399654\ttrain-mae:0.30636\ttrain-rmse:0.391275\n",
+ "[2993]\teval-mae:0.312543\teval-rmse:0.399649\ttrain-mae:0.306356\ttrain-rmse:0.39127\n",
+ "[2994]\teval-mae:0.312543\teval-rmse:0.39965\ttrain-mae:0.306354\ttrain-rmse:0.391268\n",
+ "[2995]\teval-mae:0.31254\teval-rmse:0.399646\ttrain-mae:0.306351\ttrain-rmse:0.391264\n",
+ "[2996]\teval-mae:0.312539\teval-rmse:0.399645\ttrain-mae:0.306348\ttrain-rmse:0.39126\n",
+ "[2997]\teval-mae:0.312539\teval-rmse:0.399645\ttrain-mae:0.306347\ttrain-rmse:0.391259\n",
+ "[2998]\teval-mae:0.312537\teval-rmse:0.399643\ttrain-mae:0.306343\ttrain-rmse:0.391254\n",
+ "[2999]\teval-mae:0.312536\teval-rmse:0.399642\ttrain-mae:0.306341\ttrain-rmse:0.391252\n"
+ ]
}
],
"source": [
"# Train model\n",
- "xgb.train(param,dtrain,num_round,watchlist)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# this is where your model is being trained\n",
- "\n",
- "reg.fit(x_train,y_train)"
+ "model = xgb.train(param,dtrain,num_round,watchlist)"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# for linear models you can enable the following comment and check out all the coefficients of yout linear model\n",
- "#reg.coef_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# the 20% of the test data that are used for testing purpose\n",
- "#a=reg.predict(x_test)\n",
- "b=reg.predict(x_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"metadata": {},
- "outputs": [],
- "source": [
- "# ensuring that the output has the correct dimensions\n",
- "#a.shape = (a.size, 1)\n",
- "b.shape = (b.size, 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# calculate the mean square error (lower the error better yourmodel is)\n",
- "#TestRMSE = np.sqrt(np.mean((a-y_test.values)**2))\n",
- "#display('Test RMSE = {}'.format(TestRMSE))\n",
- "\n",
- "TrainRMSE = np.sqrt(np.mean((b-y_train.values)**2))\n",
- "display('Train RMSE = {}'.format(TrainRMSE))\n",
- "\n",
- "#TestMAE = np.mean(np.abs(a-y_test.values))\n",
- "#display('Test MAE = {}'.format(TestMAE))\n",
- "\n",
- "TrainMAE = np.mean(np.abs(b-y_train.values))\n",
- "display('Train MAE = {}'.format(TrainMAE))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# R squared metric\n",
- "display(\"Test R squared = {}\".format(r2_score(y_test,a)))\n",
- "display(\"Train R squared = {}\".format(r2_score(y_train,b)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# max_depth paramater tuning for the decision tree\n",
- "max_depths = np.linspace(1, 32, 32, endpoint=True)\n",
- "train_results = []\n",
- "test_results = []\n",
- "for max_depth in max_depths:\n",
- " # Create the tree object\n",
- " dt = tree.DecisionTreeRegressor(max_depth=max_depth)\n",
- " \n",
- " # Train the tree\n",
- " dt.fit(x_train, y_train)\n",
- " \n",
- " # Predict the values for the training set\n",
- " train_pred = dt.predict(x_train) \n",
- " train_pred.shape = (train_pred.size,1)\n",
- " \n",
- " # Calculate RMSE and add to the train_results array\n",
- " TrainRMSE = np.sqrt(np.mean((train_pred-y_train.values)**2))\n",
- " train_results.append(TrainRMSE) \n",
- " \n",
- " # Predict the values for the test set\n",
- " test_pred = dt.predict(x_test) \n",
- " test_pred.shape = (test_pred.size,1)\n",
- " \n",
- " # Calculate RMSE and add to the train_results array\n",
- " TestRMSE = np.sqrt(np.mean((test_pred-y_test.values)**2))\n",
- " test_results.append(TestRMSE)\n",
- "\n",
- "from matplotlib.legend_handler import HandlerLine2D\n",
- "line1, = plt.plot(max_depths, train_results, 'b', label = \"Train RMSE\")\n",
- "line2, = plt.plot(max_depths, test_results, 'r', label = \"Test RMSE\")\n",
- "\n",
- "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
- "plt.ylabel('RMSE')\n",
- "plt.xlabel('Tree depth')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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uu+46Dh48SGpqKq1bt/buN3/+fCZNmpTvNfbv309ERASqypIlS4iJiamAyH4S6knJn8+B2SIyGWf5rj/gv9avMcYEkNWrVzN37lxiY2OJj3f+3v7jH//IzJkzSU1NpVq1arRs2ZLp06cDTinzGTNmEBsbi6rypz/9ifDwcMA5J7V3716uv/76fK8xaNAgDh8+jKoSHx/vHauiVLmkpKpfichsnHNLAK+rapFLd8YYEyi6du2Kv+sA+vbt67d/eHg4y5cv97stMjKS9PT0Qu0rV668sEleoJBOSkX9z5GqvoifS8Ptf5SMMaZyVaVLwo0xxgQ4S0rGGGMChiUlY4wxAcOSkjHGmIBhSckYY0zAsKRkjDEmYFhSMsYYEzAsKRljjAkYlpSMMcYEDEtKxhhjAoYlJWOMKQN79+6le/fuREVFER0dzdSpUwFYuHAh0dHRVKtWjQ0bNnj7Z2VlMWTIEGJjY4mKisp3t+5hw4bRuHHjQnfonjhxIs2aNSM+Pp74+Hg+/vjjigmuAgVFUhKR/xGRBSLyHxHZISIfi0jbMhh3toh8KyIp7kf8ufcyxpjCwsLCmDJlCjt37mTt2rW88sor7Nixg5iYGN57771CxfgWLlzI2bNn2bp1Kxs3buTVV18lLS0NgKSkJJYtW+b3dR599FFSUlJISUkp8kaswSzgb8jq1j9aDMxR1TvctnigCfBNCfavrqo5xXQZq6qLSjqf01k5RI77qKTdg97o2GySLN6QZfGWjbTJ/YiIiCAiIgKAevXqERUVRXp6Oj179vS7j4iQmZlJdnY2p0+fpkaNGlxyySUAdOvWzZugqppgOFLqDmSpqreoh6qmANVF5MO8NhGZJiJJ7uM0EZkgIv8CHheRdT79IkVkS8VN3xhT1aSlpbFp0yauueaaIvvcdttt1KlTh4iICC6//HLGjBlDw4YNzzn2tGnTiIuLY9iwYRw/frwspx0QAv5ICYgBNpZivzOq2hVARG4Xkdaquge4HXjHp98fRGQCsAIYp6pnCw4kIvcB9wGEhzdiQmx2KaYTnJrUdv66rCos3tBWXvF6PB7v49OnTzNq1CjuuecevvrqK2/7iRMn2LhxIxkZGQBs3bqVI0eOMH/+fE6ePMmoUaOoW7cuTZs2BeDAgQNkZmbmGzsuLo6ZM2ciIsyaNYs777yTJ554osh5ZWRk5Ns/GARDUiqtt30evwMMACbjJKXb3fbfAQeAGsAM4AngmYIDqeoMdzuXt26jU7aG8pctv9Gx2Vi8ocviLRt5JdazsrJITExkxIgR+cqVAzRo0ICOHTvSqVMnwDmnNGTIEHr06AHABx98QFhYGAkJzlhpaWnUqVPH+7yg1q1bk5iYWOR2cJJlcdsDUTD8NG4HbvPTnk3+5cdaBbZn+jx+G1goIu8Bqqq7cB7sd7efFZE3gDHnmkzti6qTOrlfSece9Dwej/cXriqweENbecarqgwfPpyoqKhCCcmfyy+/nJUrVzJ48GBOnTrF2rVreeSRR4rdZ//+/d7zVosXLy50dV4oCIZzSiuBmiJyb16DiHQGqgPtRKSmiNQHflXUAKr6HyAHeBKfIygRiXA/C3AzsK1cIjDGhLzVq1czd+5cVq5cme+S7cWLF9O8eXPWrFlDv3796N27NwAPPPAAGRkZxMTE0LlzZ4YOHUpcXBwAAwcOpEuXLqSmptK8eXNmzpwJwOOPP05sbCxxcXGsWrWKP//5z5UWb3kJ+CMlVVUR6Q+8JCLjgDNAGvAIzrLcFmAXsOkcQ70NJAOtfNrmiUgjQIAUYETZzt4YU1V07doVVfW7rX///oXa6taty8KFC/32nz9/vt/2uXPnln6CQSLgkxKAqu7DOSdU0OPuR8H+kX7aXgBeKNB2QxlN0RhjTBkIhuU7Y4wxVYQlJWOMMQHDkpIxxpiAYUnJGGNMwLCkZIwxJmBYUjLGGBMwLCkZY4wJGJaUjDHGBAxLSsYYYwKGJSVjjDmH8y11fvToUbp3707dunV58MEH8421ceNGYmNjadOmDQ8//LD31kRFjVXVBEVSEpH+IqIiclUZjmml0I0xJXK+pc5r1arFs88+ywsvvFBorJEjRzJjxgx27drFrl27vGXPixqrqgmKe98BA4F/AXcAE303lKDceXHOqxQ6WDn0UGfxhrbSxFuaUud16tSha9eu7N69O1/7/v37+eGHH+jSpQsAd999N0uWLKFPnz5ERUWVIqLQE/BHSiJSF/glMBwnKSEiCSKySkT+Dmx12waLyDr3qOdVEanutv9NRDaIyHYRebqy4jDGhIaSlDovSnp6Os2bN/c+b968Oenp6WU5vaAXDEdKNwPLVPUbETkmIh3c9quBGFX9VkSicKrJ/lJVs0Tkr8Ag4E1gvKoec5PUChGJU9Ut7hjnLIUOVg7dymWHLov33EpT6jzP119/TXp6uneMr7/+muPHj3ufb9myhWPHjuV7jaLGKg0rh14+BgIvuY8XuM8/Atap6rdu+6+AjsB6p14ftYFD7rYBblIJAyKAdjg1mEpUCh2sHLrFG7os3nMrTalz775paWRkZHhLkl955ZW89NJL3uf79+8nNjY2X8nyosYqDSuHXsZE5DLgBiBGRBSn2qwCH5O/3LkAc1T1dwX2b4VT4ryzqh4Xkdm4ZdNLUwodrBx6qLN4Q1tp4z3fUudFiYiIoF69eqxdu5ZrrrmGN998k4ceeqjU44WiQD+ndBvwpqq2VNVIVW0BfAt0LdBvBXCbiDQGEJGGItISuAQnef1XRJoAffJ2sFLoxpiSOt9S5wCRkZE89thjzJ49m+bNm7Njxw4A/va3v3HPPffQpk0bfvazn9Gnj/O2VNxYVUlAHynhLNVNLtD2LjAS+E9eg6ruEJHfA8tFpBqQBTygqmtFZBOwHdgDrPYZx0qhG2NK5HxLnYOzdOdPp06d2Lat8N/A/fv3L3KsqiSgk5KqJvhpexl42U/728DbftqTihjbSqEbY0yACfTlO2OMMVWIJSVjjDEBw5KSMcaYgGFJyRhjTMCwpGSMMSZgnHdSEpFLRSSuPCZjjDGmaitRUhIRj4hcIiINgc3AGyLyYvlOzRhjTFVT0iOl+qr6A3AL8IaqdgR6lN+0jDHGVEUlTUph7m15BgAfluN8jDHGVGElTUrPAJ8A/1HV9SLSGthVftMyxhhTFZUoKanqQlWNU9WR7vM9qnpr+U7NGGMq3969e+nevTtRUVFER0czdepUABYuXEh0dDTVqlVjw4YN+faZNGkSbdq04corr+STTz4B4MyZM1x99dW0b9+e6OhonnrqKW//lStX0qFDB2JiYhgyZAjZ2VWnxlVBJb3Qoa2IrBCRbe7zOPcGqJVKRPxWwXKr0G5xq81uFpHXRaSBu80jIp18Hqe61WpT8u4ybowxecLCwpgyZQo7d+5k7dq1vPLKK+zYsYOYmBjee+89unXrlq//jh07WLBgAdu3b2fZsmXcf//95OTkULNmTVauXMnmzZtJSUlh2bJlrF27ltzcXIYMGcKCBQvYtm0bLVu2ZM6cOZUUbeUr6Q1ZXwPGAq8CqOoWtxT5c+U1sdISkV8DjwJ9VDXdrTg7BGgCnPCzyyBV3eCn3a/TWTlEjvuobCYbBEbHZpNk8YYsi/fc0ib3IyIiAoB69eoRFRVFeno6PXv29Nv//fff54477qBmzZq0atWKNm3asG7dOrp06ULdunUBp2BgVlYWIsLRo0epWbMmbdu2BaBnz55MmjSJ4cOHX0Ckwauk55QuVtV1BdoC9fhyPDBGVdMBVDVHVWepamolz8sYE+TS0tLYtGkT11xzTZF90tPTadGihfd58+bNSU9PByAnJ4f4+HgaN25Mz549ueaaawgPDycrK8u7BLho0SL27t1bvoEEsJIeKR0RkZ/hVH1FRG4D9he/S6WJBr46j/5viEgOTp2m59RP0RS3nPp9AOHhjZgQG6j5uOw1qe38dVlVWLyhrTTxejweAE6fPs2oUaO45557+Oqrn95iTpw4wcaNG8nIcM4mfP/99+zcudO73/79+9m+fTvh4eEAvPTSS2RkZPDkk09y1VVX0apVKx5//HGGDRtGVlYWnTp14syZM979L0RGRkaZjFORSpqUHgBmAFeJSDpO9ddB5TarMiIiscBcoB7wv27NJV+D3CW+ejhJ6S7gzYLjqOoMnPi5vHUbnbI1oMtQlanRsdlYvKHL4j23tEEJZGVlkZiYyIgRIwqVQ2/QoAEdO3akU6dOAKxZswaAhIQEwLnooVevXnTp0iXffhs3buTo0aMMHTqUhIQEHnjgAQCWL1/O2bNnvftfCI/HUybjVChVLfYDZ4lvgPu4DlDvXPtU1AeQ4aftC6B7gbZpQJL72AN08rNfEjDtXK/Ztm1brUpWrVpV2VOoUBZvaCtNvLm5uXrXXXfpqFGj/G6//vrrdf369d7n27Zt07i4OD1z5ozu2bNHW7VqpdnZ2Xro0CE9fvy4qqqeOnVKu3btqh988IGqqh48eFBVVc+cOaM33HCDrlix4rzn6U8gfX+BDVqC9/Vz/smgqrki8iDwjqpmlmVCLCeTgBdE5CZV/d5tq12wk4iEAQ1U9YiIXAQkAv+swHkaY4LA6tWrmTt3LrGxscTHxwPwxz/+kbNnz/LQQw9x+PBh+vXrR3x8PJ988gnR0dEMGDCAdu3aERYWxiuvvEL16tXZv38/Q4YMIScnh9zcXAYMGEBiYiIAycnJfPjhh+Tm5jJy5EhuuKHqFsYu6XHspyIyBqfcuDcxqeqxcplVyV0sIt/7PH9RVV8UkUbAP9wr704A23D++ddXTeATNyFVx0lIr1XEpI0xwaNr1655qymF9O/f32/7+PHjGT9+fL62uLg4Nm3a5Ld/cnIyycnJFzbREFHSpDTM/fyAT5sCrct2OudHVf1ePaiqcwC/F/qraoLP047lMC1jjDGlVKKkpKqtynsixhhjTImSkojc7a9dVQtdqWaMMcaUVkmX7zr7PK4F/Arnf4EsKRljjCkzJV2+e8j3uYjUx/n/H2OMMabMnHc5dNcp4IqynIgxxhhT0nNKH+DeYggnkbUDFpbXpIwxxlRNJT2n9ILP42zgO59/TDXGGGPKREmX7/qq6mfux2pV/V5E/lSuMzPGGFPllDQp+Ssc0qcsJ2KMMcYUu3wnIiOB+4HWIrLFZ1M9YHV5TswYY0zVc64jpb8DNwJL3c95Hx1VdXA5z80YY0ps2LBhNG7cmJiYGG/b5s2b6dKlC7Gxsdx4441kZjq37jx69Cjdu3enbt26PPjgg37H+81vfpNvrCeffJK4uDji4+Pp1asX+/btK9+Aqqhik5Kq/ldV01R1oKp+B5zGuQqvrohcXty+IpIjIikisk1EForIxW77v881KRHJKPD8URE54/5/VJkREY+IpLrzTBGRxmU5vjGm4iQlJbFs2bJ8bffccw+TJ09m69at9O/fn7ffdkqq1apVi2effZYXXnjB31C899573tLlecaOHcuWLVtISUkhMTGRZ555pnwCqeJKekn4jcCLQFPgENAS2IlT5bUop1U13t1/HjAC5y7evyjFPAcC64H+wGw/8wtT1dKWzxykqhtK2vl0Vg6R4z4q5UsFn9Gx2SRZvCErVOJNm9yPbt26kZaWlq89NTWVbt26AdCzZ09vIqlTpw5du3Zl9+7dhcbKyMjgxRdfZMaMGQwYMMDbfskll3gfZ2ZmIiLlEIkp6YUOzwHXAt+4N2f9Fed3TukLoA38dBQkInVFZIWIfCUiW0XkJn87umXY6wK/x0lOee1J7hHYB8Byt22siKwXkS0i8rRP3yUislFEtrulzY0xVUBMTAxLly4FYOHChRw6dOic+zz55JOMHj2aiy++uNC28ePH06JFC+bNm2dHSuWkpP+nlKWqR0WkmohUU9VVJb0k3C2m1wdYVmDTGaC/qv4gIuHAWhFZqoULlwwE5uMktitFpLGq5v1kdQHiVPWYiPTCucvE1YAAS0Wkm6p+Dgxz+9QG1ovIu6p61B3jDRHJwSmH/pyf18dNZPcBhIc3YkJsaQ/Kgk+T2s5f01WFxRucPB4PAAcOHCAzM9P7fMSIETz33HOMHTuWX/7yl4SFhXm3AXz99dekp6d723Z0J9Z1AAAeDUlEQVTv3s2XX37JTTfdxNq1a/ONBc7RVs+ePZk3bx5jxoxh6NChFRNgKWVkZOSbfzAoaVI6ISJ1cRLDPBE5hPNPtMWpLSIp7uMvgJkFtgvwRxHpBuQCzYAmwIEC/e7ASV65IvIe8P+AV9xtn/oUGuzlfuRV0aqLk6Q+Bx4WkbxqXC3c9qM4S3fpIlIPJyndhZ+bzKrqDGAGwOWt2+iUrSX9sgW/0bHZWLyhK1TiTRuU4HxOS6NOnTokJCR4t919t1Pk4JtvvmHt2rX5tqWlpZGRkeFt27lzJ2lpaSQlJZGdnc2hQ4eYOHFioTf2Vq1a0a9fP+bM8Vu2LWB4PJ588QaDkv403oRzkcMjwCCgPnCuY1fvOaUiDAIa4VzJlyUiaTh3IPcSkTicBPKpu35bA9jDT0nJtzy7AJNU9dUCYyQAPYAuqnpKRDx5r6Oq6e7nkyLyd5yjrGLvfF77ouqkTu5XXJeQ4vF4vL/wVYHFG1oOHTpE48aNyc3N5bnnnuPGG28stv/IkSMZOXIk4CSsxMREb0LatWsXV1zh3PJz6dKlXHXVVeU696qqpHcJzxSRlsAVqjrHvZKu+gW+dn3gkJuQuuNcPFHQQGCiqk7KaxCRb925FPQJ8KyIzFPVDBFpBmS5r3PcTUhX4Zwby1tWbKCqR9yS6Ik4JdGNMUFo4MCBeDwejhw5QvPmzXn66afJyMjglVecv2FvueUWevfu7e0fGRnJDz/8wI8//siSJUtYvnw57dq1K3L8cePGkZqaSrVq1WjZsiXTp08v95iqopJefXcvzjmVhsDPcJbapuNc8FBa84APRGQDkAJ87afPHRS+c8Rit/2gb6OqLheRKGCNe1SVAQzGOZc1wv3n31RgrbtLTeATNyFVx0lIr11APMaYSjR//ny/7aNGjfI+9l2GK3ilXkGRkZFs27bN+/zdd9+9oPmZkinp8t0DOEtbXwKo6q5z/U+PqtYtrl1Vj+BcqFBcn0Jl2FX1MZ+nswtsmwpM9TNkUbdE6lhEuzHGmEpQ0kvCz6rqj3lP3KWvQlepGWOMMReipEnpMxH5X5wr6nri1FL6oPymZYwxpioqaVIaBxwGtgK/BT7G+WdWY4wxpsyc6y7hl6vq/6lqLs5FAHYhgDHGmHJzriOlJXkPRMQuPTHGGFOuzpWUfO842Lo8J2KMMcacKylpEY+NMcaYMneu/1NqLyI/4Bwx1XYf4z5XVb2k6F2NMcaY81NsUlLVC72VkDHGGFNiJb0k3BhjjCl3lpSMMUFj2LBhNG7cmJiYGG9bSkoK1157LfHx8XTq1Il169YBzn3u6tevT3x8PPHx8YWK8uXk5PDzn/+cxMREb9vKlSvp0KEDMTExDBkyhOzs4K81FWzKLSmJSI6IpIjINrdC7MVu+79LsG+Gz+O2IvKxiOwWkZ0i8o6INCmjOXpEJNWdZ8q57udnjKlcSUlJLFuWv17o448/zlNPPUVKSgrPPPMMjz/+uHfbddddR0pKCikpKUyYMCHfflOnTiUqKsr7PDc3lyFDhrBgwQK2bdtGy5YtA75eUigqz+pe3npKIjIPGAG8qKq/KOkAIlIL+Ah4TFU/cNu649RhOljcvj5jVFfVnGK6DFLVDSWd0+msHCLHfVTS7kFvdGw2SRZvyAqmeNMm96Nbt26F7u4tIvzwg3MN1n//+1+aNm16zrG+//57PvroI8aPH8+LL74IwNGjR6lZsyZt27YFnCqzkyZNYvjw4WUbiClWRS3ffQG0gZ+OgkSkroisEJGvRGSriNzkZ787gTV5CQlAVVep6jYRSRKRaXntIvKhW9APEckQkWdE5Evgf0XkHZ9+CSJi9+0zJkS89NJLjB07lhYtWjBmzBgmTfKWX2PNmjW0b9+ePn36sH37dm/7I488wvPPP0+1aj+9BYaHh5OVlcWGDc7fqIsWLWLv3r0VF4gByvdICfDeUbwPTl0jX2dwypz/ICLhwFoRWaqqvv8PFQNsLMXL1gG2qeoE9/X3iEgdVc0Ebgfe9un7hojk4JRDf67A6+fFcB9OPSnCwxsxIbbqrDM3qe38NV1VWLyBK68W0oEDB8jMzPQ+f/nllxk+fDjXX389q1at4pZbbmHKlClkZmby1ltvUbt2bdauXUvv3r2ZPn06kyZNIisri5MnT5KSksLRo0e9Yz3++OMMGzaMrKwsOnXqxJkzZwqVQg8mGRkZQTd/8fMeXDYDO2/0W92nXwCjVfVHEclQ1bpucb0/A92AXOBKoJWqHvDp8yLwnVsnqeD4SUAnVX3Qff4h8IKqekQkG6iZt2wnIjOAlcAinHLq0W4J9Gaqmi4i9XCS0luqWmw59Mtbt9FqA/yVbApNo2OzmbK13P92CRgWb+BKm9zP+eyWKc8rwFe/fn1OnDiBiKCq1K9f37uc5ysyMpKpU6eydu1a5s6dS1hYGGfOnOGHH37glltu4a233srXf/ny5bz++uu88847hcYKFh6Ph4SEhMqeBgAislFVO52rX4WcUyrCIJxzQx3dkuhpQK0CfbYD1xexfzb5lx999z1T4DzS2ziFCo8B61X1JICqprufT4rI33EKGRablGpfVJ1U95ejKvB4PKQNSqjsaVQYizf4NG3alM8++4yEhARWrlzJFVdcAThHVE2aNEFEWLduHbm5uVxyySVMmjTJu8Tn8Xh44YUXvAnp0KFDNG7cmLNnz/KnP/2J8ePHV1pcVVVl/olUHzjkJqTuQEs/ff4O/E5E+qnqRwAi8msgHUgD7heRajjl2a8u5rU8wEzgXtylO3dZr4GqHnGP2hJxSqIbYwLUwIED8Xg8HDlyhObNm/P000/z2muvMWrUKLKzs6lVqxYzZswAnHNCf/vb3wgLC6N27dosWLCAH3/8sdjxk5OT+fDDD8nNzWXkyJHccMMNFRGW8VGZSWke8IGIbABSgK8LdlDV0yKSCLwkIi8BWcAWYBSwDfgWZ4lwG/BVUS+kqjnu8l4SMMRtrgl84iak6jgJyUpzGBPA5s+f77d948bCp54ffPBBHnzwwXxtBc+vJCQk5FveSk5OJjk5+YLnaUqv3JKSqtYtrl1VjwBdzrWvqn4N/LqIlxlU0td2zz096PM8E+hYxLjGGGMqgd3RwRhjTMCwpGSMMSZgWFIyxhgTMCwpGWOMCRiWlIwxxgQMS0rGGGMChiUlY4wxAcOSkjHGmIBhSckYY0zAsKRkjDkv/kqST5w4kWbNmnlLj3/88ccA/PjjjwwdOpTY2Fjat2+f7zY/CQkJXHnlld59Dh06BMD//d//0b17d37+858TFxfnHctUDSGVlESkv4ioiFxV2XMxJlT5K0kO8Oijj3pLj/ft2xeA115zbie5detWPv30U0aPHk1ubq53n3nz5nn3ady4MQDPPfccAwYMYNOmTSxYsID777+/AqIygSKkkhIwEPgXcEdlT8SYUNWtWzcaNmxYor47duzgV7/6FQCNGzemQYMG3squRSlNeXMTOoKjulcJiEhd4JdAd2ApMNEtazENpybTtzhJeJaqLhKRjsCLQF3gCJCkqvvP9Tqns3KIHPdROUUReEbHZpNk8Yas8403rZhaYtOmTePNN9+kU6dOTJkyhUsvvZT27dvz/vvvc8cdd7B37142btzI3r17ufpqp9LM0KFDqV69Orfeeiu///3vEREmTpxIr169+Mtf/kJmZib//KdVlKlKyq3ybEUTkcFAd1UdLiL/xrkjeGtgGE6tpMbATpyaSu8DnwE3qephEbkd6K2qw4oY27ccescJL1WdChdNasPB05U9i4pj8RYvtll9wCmg97vf/Y433ngDgGPHjlG/fn1EhFmzZnH06FGeeOIJcnJymD59Ops2baJJkybk5OSQmJhI165dOXz4MI0aNeLUqVM89dRT9OjRg969e3srvQ4YMIDt27eTnJzMrFmzqFbtwhd2MjIyqFvXbwGDkBRI8Xbv3r1ElWdDKSl9BLykqp+KyMNAC+AiYLOqvuH2eQ+ncODXwL9xSqODU09pv6r2OtfrWDn00GbxFq+okuT5+hSz7Re/+AWvv/467dq1y9c+e/ZsNmzYwLRp04iOjmbZsmW0aNECgNatW7N27VrvOacLEUjlwStCIMUbCOXQK4yIXAbcAMSIiOIkGQUWF7ULsF1V/dZzKo6VQw9tFm/p7N+/n4iICAAWL17svTLv1KlTqCp16tTh008/JSwsjHbt2pGdnc2JEycIDw8nKyuLDz/8kB49egBw+eWXs2LFCpKSkti5cydnzpyhUaNGFzxHExxCIikBtwFvqupv8xpE5DOcc0W3isgcoBGQgHOklAo0EpEuqrrGrT7bVlW3V/zUjQku/kqSezweUlJSEBEiIyN59dVXATh06BC9e/emWrVqNGvWjLlz5wJw9uxZevfuTVZWFjk5OfTo0YN7770XgClTpnDvvffy5z//GRFh9uzZiEilxWsqVqgkpYHA5AJt7wJRwPc45dK/Ab4E/quqP4rIbcDLIlIf5+vwEmBJyZhz8FeSfPjw4X77RkZGkpqaWqi9Tp06fkuYA7Rr147Vq1df2CRN0AqJpKSqCX7aXgbnqjxVzXCX+NYBW93tKUC3ipynMcaY4oVEUjqHD0WkAVADeFZVD1T2hIwxxvgX8knJ31GUMcaYwBRqd3QwxhgTxCwpGWOMCRiWlIwxxgQMS0rGGGMChiUlY4wxAcOSkjHGmIBhSckYY0zAsKRkjDEmYFhSMsYUa9iwYTRu3Nh752+AiRMn0qxZM+Lj44mPj+fjjz/2btuyZQtdunQhOjqa2NhYzpw5w8mTJ7194+PjCQ8P55FHHgHg888/p0OHDoSFhbFo0aIKj88ElqBKSiIyXkS2i8gWEUkRkWuK6ZskItPOMd6vRSRVRHaLyLiyn7ExwS8pKYlly5YVan/00UdJSUkhJSWFvn37ApCdnc3gwYOZPn0627dvx+PxcNFFF1GvXj1v35SUFFq2bMktt9wCOKUqZs+ezZ133lmhcZnAFDS3GRKRLjgVZDuo6lkRCce5n11px6sOvAL0xLmT+HoRWaqqO4rbz8qhhzaLN7+0yf3o1q0baWlpJRpv+fLlxMXF0b59ewAuu+yyQn127drFoUOHuO666wDnTuJAmVSWNcEvmH4KIoAjqnoWQFWPqOo+EUlzExQi0klEPAV3FJEmIrJYRDa7H78ArgZ2q+oeVf0RWADcVHHhGBPcpk2bRlxcHMOGDeP48eMAfPPNN4gIvXv3pkOHDjz//POF9ps/fz6333671UgyfgXNkRKwHJggIt8A/wTeVtXPSrjvy8BnqtrfPUKqi3OEtNenz/eA3+VAEbkPuA8gPLwRE2KzSxlC8GlS2/lruqqwePPzeDwAHDhwgMzMTO/zuLg4Zs6ciYgwa9Ys7rzzTp544glSU1P55z//yfTp06lZsyajR4+mevXqdOzY0TvmrFmz+N3vfucdK8+BAwfYvn074eHhZR2mV0ZGRqHXDWXBGG/QJCW3JlJH4DqgO/D2eZwHugG42x0nB/iv+P8zTYt47RnADIDLW7fRKVuD5st2wUbHZmPxhq5zxZtXKj0tLY06deqQkJBQqE/r1q1JTEwkISGBAwcOcPr0aW66yVl0WL9+Pbm5ud79Nm/eTI0aNfjtb39baJzZs2cTHR3t9zXKisfjKdfxA00wxhtUv31uQvEAHhHZCgwBsvlpGbLWeQz3PdDC53lzYN+5dqp9UXVSJ/c7j5cJbh6Px/vGVBVYvCWzf/9+IiIiAFi8eLH3yrzevXvz/PPPc+rUKWrUqMFnn33Go48+6t1v/vz5DBw4sEzmbkJT0CQlEbkSyFXVXW5TPPAdUBvoCPwDuLWI3VcAI4GX3OW7OsB64AoRaQWkA3cAdvmPMQUMHDgQj8fDkSNHaN68OU8//TQej4eUlBREhMjISF599VUALr30Uh577DE6d+6MiNC3b1/69fvpj7h33nkn3+Xj4BxN9e/fn+PHj/PBBx/w1FNPsX379gqN0QSOoElKOOeB/uJWkc0GduOc54kCZorI/wJfFrHvKGCGiAwHcoCRqrpGRB4EPgGqA7NU1X4TjClg/vz5hdqGDx9eZP/BgwczePBgv9v27NlTqK1z5858//33pZ+gCSlBk5RUdSPwCz+bvgDa+uk/G5jtPj6InyvrVPVj4OOC7cYYYypHMF0SbowxJsRZUjLGGBMwLCkZY4wJGJaUjDHGBAxLSsYYYwKGJSVjjDEBw5KSMcaYgGFJyRhjTMCwpGSMMSZgWFIyxhgTMCwpmVKJjIwkNjaW+Ph4OnXqBMCTTz5JXFwc8fHx9OrVi3378t90ff369VSvXp1FixZ525544gliYmKIiYnh7bffrtAYjDGBp9ySkoioiMz1eR4mIodF5EP3+W+KqockIhkFnj8qImdEpH4Zz9EjIqkikuJ+NC7L8UPdqlWrSElJYcOGDQCMHTuWLVu2kJKSQmJiIs8884y3b05ODk888QS9e/f2tn300Ud89dVXpKSk8OWXX5KcnMwPP/xQ4XEYYwJHed6QNROIEZHaqnoap9Jret5GVV0KLC3hWANxSk30x73Jqi8RCVPV0pYLHaSqG0ra+XRWDpHjPirlSwWf0bHZJPnEm1ZMLalLLrnE+zgzMzNfueu//OUv3Hrrraxfv97btmPHDq6//nrCwsIICwujffv2LFu2jAEDBpRxFMaYYFHey3f/APLexQYC3nvgi0iSiExzH7cSkTUisl5EnvUdQER+hlO24vfuGL77LxSRD3BKpSMiY90xtojI0z59l4jIRhHZ7pY2NxdIROjVqxcdO3ZkxowZ3vbx48fTokUL5s2b5z1SSk9PZ/HixYwYMSLfGO3bt+cf//gHp06d4siRI6xatYq9e/dijKm6yrt0xQJggrtkFwfMwilnXtBU4G+q+qaIPFBgW14y+wK4UkQaq+ohd1sXIE5Vj4lIL+AK4GpAgKUi0k1VPweGuX1qA+tF5F1VPeqO8YaI5ADvAs+paqGS6G4iuw8gPLwRE2JLe1AWfJrUdo6W8ng8HgCSk5MJDw/n+PHjjBkzhtOnT9O+fXt69uxJz549mTdvHmPGjGHo0KFMnDiR22+/nS+++IIDBw6wfft2wsPDqVGjBlFRUcTFxdGgQQNat27Nt99+632NypCRkVGpr1/RLN7QFozxlmtSUtUtIhKJk1iKq1v0S36qGjsX+JPPtjuA/qqaKyLvAf8PeMXd9qmqHnMf93I/NrnP6+Ikqc+Bh0Wkv9vewm0/irN0ly4i9XCS0l3Am37imAHMALi8dRudsjVoylBdsNGx2fjG66909ubNm8nKyiIh4adtrVq1ol+/fsyZM4fvvvuO559/HoAjR47w1Vdf0b59e26++eZ8+9x555307ds3X1tF83g8lfr6Fc3iDW3BGG9FvLsuBV4AEoDLiunn7wglDieBfOqen6gB7OGnpJTp2x2YpKqvFhgjAegBdFHVUyLiAWoBqGq6+/mkiPwd5yirUFLyVfui6qQWc14l1Hg8nkKJKDMzk9zcXOrVq0dmZibLly9nwoQJ7Nq1iyuuuAKApUuXctVVVwHw7bffevdNSkoiMTGRm2++mZycHE6cOMFll13Gli1b2LJlC7169aqw2IwxgaciktIs4L+qutVNEP6sxjkiegsY5NM+EJioqpPyGkTkWxFp6WeMT4BnRWSeqmaISDMgC6gPHHcT0lXAte44YUADVT0iIhcBicA/LyjSKuLgwYP07+8ceGZnZ3PnnXfy61//mltvvZXU1FSqVatGy5YtmT59erHjZGVlcd11zmruJZdcwltvvUVYWNU5CjXGFFbu7wCq+j3OOaPijAL+LiKjcJbR8twB9CnQd7HbfrDA6ywXkShgjXtUlQEMBpYBI0RkC5AKrHV3qQl84iak6jgJ6bXzi65qat26NZs3by7U/u677/rpnd/s2bO9j2vVqsWOHTvKcmrGmCBXbklJVev6afMAHvfxbNzLu1X1W5yLFvJMdttb+RnjMZ+nswtsm4r/BFgwseXpWES7McaYSmB3dDDGGBMwLCkZY4wJGJaUjDHGBAxLSsYYYwKGJSVjjDEBw5KSMcaYgGFJyRhjTMCwpGSMMSZgWFIyxhgTMCwpGWOMCRiWlIwxxgQMS0rGGGMChiUlY4wxAcOSkjHGmIAhqoUKvppiiMhJnLpMVUU4cKSyJ1GBLN7QZvFWnpaq2uhcnazM5/lLVdVOlT2JiiIiGyze0GXxhrZgjNeW74wxxgQMS0rGGGMChiWl8zejsidQwSze0Gbxhragi9cudDDGGBMw7EjJGGNMwLCkZIwxJmBYUiohEfm1iKSKyG4RGVfZ87kQIpImIltFJEVENrhtDUXkUxHZ5X6+1G0XEXnZjXuLiHTwGWeI23+XiAyprHgKEpFZInJIRLb5tJVZfCLS0f367Xb3lYqNML8i4p0oIunu9zhFRPr6bPudO/dUEent0+73Z1xEWonIl+7X4W0RqVFx0RUmIi1EZJWI7BSR7SIyym0Pye9xMfGG5vdYVe3jHB9AdeA/QGugBrAZaFfZ87qAeNKA8AJtzwPj3MfjgD+5j/sC/wAEuBb40m1vCOxxP1/qPr60smNz59YN6ABsK4/4gHVAF3effwB9AjDeicAYP33buT+/NYFW7s919eJ+xoF3gDvcx9OBkZUcbwTQwX1cD/jGjSskv8fFxBuS32M7UiqZq4HdqrpHVX8EFgA3VfKcytpNwBz38RzgZp/2N9WxFmggIhFAb+BTVT2mqseBT4FfV/Sk/VHVz4FjBZrLJD532yWqukad3+A3fcaqFEXEW5SbgAWqelZVvwV24/x8+/0Zd48QbgAWufv7fu0qharuV9Wv3McngZ1AM0L0e1xMvEUJ6u+xJaWSaQbs9Xn+PcX/UAQ6BZaLyEYRuc9ta6Kq+8H5JQAau+1FxR5sX5Oyiq+Z+7hgeyB60F2umpW3lMX5x3sZcEJVswu0BwQRiQR+DnxJFfgeF4gXQvB7bEmpZPytJwfztfS/VNUOQB/gARHpVkzfomIPla/J+cYXLHH/DfgZEA/sB6a47SETr4jUBd4FHlHVH4rr6qct6GL2E29Ifo8tKZXM90ALn+fNgX2VNJcLpqr73M+HgMU4h/UH3WUL3M+H3O5FxR5sX5Oyiu9793HB9oCiqgdVNUdVc4HXcL7HcP7xHsFZ7gor0F6pROQinDfoear6ntscst9jf/GG6vfYklLJrAeucK9QqQHcASyt5DmViojUEZF6eY+BXsA2nHjyrj4aArzvPl4K3O1ewXQt8F93aeQToJeIXOouG/Ry2wJVmcTnbjspIte6a/F3+4wVMPLenF39cb7H4MR7h4jUFJFWwBU4J/X9/oy751RWAbe5+/t+7SqF+3WfCexU1Rd9NoXk97ioeEP2e1xZV1gE2wfOFTzf4Fy9Mr6y53MBcbTGuepmM7A9LxacdeUVwC73c0O3XYBX3Li3Ap18xhqGcxJ1NzC0smPzmdd8nOWMLJy/DoeXZXxAJ5w3gP8A03DvjBJg8c5149mC8yYV4dN/vDv3VHyuKivqZ9z9mVnnfh0WAjUrOd6uOMtLW4AU96NvqH6Pi4k3JL/HdpshY4wxAcOW74wxxgQMS0rGGGMChiUlY4wxAcOSkjHGmIBhSckYY0zACDt3F2NMeRORHJzLe/PcrKpplTQdYyqNXRJuTAAQkQxVrVuBrxemP93rzJiAYct3xgQBEYkQkc/dujnbROQ6t/3XIvKViGwWkRVuW0MRWeLeqHOtiMS57RNFZIaILAfeFJHqIpIsIuvdvr+txBCNAWz5zphAUVtEUtzH36pq/wLb78S5Bc4fRKQ6cLGINMK551k3Vf1WRBq6fZ8GNqnqzSJyA07phXh3W0egq6qedu8Q/19V7SwiNYHVIrJcnXIHxlQKS0rGBIbTqhpfzPb1wCz3xpxLVDVFRBKAz/OSiKrm1VTqCtzqtq0UkctEpL67bamqnnYf9wLiRCTvnmf1ce6TZknJVBpLSsYEAVX93C0x0g+YKyLJwAn8lxgorhRBZoF+D6lqIN9I11Qxdk7JmCAgIi2BQ6r6Gs4dozsAa4Dr3TtB47N89zkwyG1LAI6o/3pDnwAj3aMvRKSte+d4YyqNHSkZExwSgLEikgVkAHer6mH3vNB7IlINp35QT2Ai8IaIbAFO8VM5h4JeByKBr9zyCIep5FLnxtgl4cYYYwKGLd8ZY4wJGJaUjDHGBAxLSsYYYwKGJSVjjDEBw5KSMcaYgGFJyRhjTMCwpGSMMSZg/H/5hr40g3eJywAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "# min_samples_leaf paramater tuning for the decision tree\n",
- "min_samples = np.linspace(5, 100, 20, endpoint=True)\n",
- "train_results = []\n",
- "test_results = []\n",
- "for n in min_samples:\n",
- " # Create the tree object\n",
- " dt = tree.DecisionTreeRegressor(min_samples_leaf=n/x_train.size)\n",
- " \n",
- " # Train the tree\n",
- " dt.fit(x_train, y_train)\n",
- " \n",
- " # Predict the values for the training set\n",
- " train_pred = dt.predict(x_train) \n",
- " train_pred.shape = (train_pred.size,1)\n",
- " \n",
- " # Calculate RMSE and add to the train_results array\n",
- " TrainRMSE = np.sqrt(np.mean((train_pred-y_train.values)**2))\n",
- " train_results.append(TrainRMSE) \n",
- " \n",
- " # Predict the values for the test set\n",
- " test_pred = dt.predict(x_test) \n",
- " test_pred.shape = (test_pred.size,1)\n",
- " \n",
- " # Calculate RMSE and add to the train_results array\n",
- " TestRMSE = np.sqrt(np.mean((test_pred-y_test.values)**2))\n",
- " test_results.append(TestRMSE)"
+ "# Plot feature importance values\n",
+ "xgb.plot_importance(model)\n",
+ "pyplot.show()"
]
},
{
@@ -1328,14 +3322,9 @@
"metadata": {},
"outputs": [],
"source": [
- "from matplotlib.legend_handler import HandlerLine2D\n",
- "line1, = plt.plot(min_samples, train_results, 'b', label = \"Train RMSE\")\n",
- "line2, = plt.plot(min_samples, test_results, 'r', label = \"Test RMSE\")\n",
- "\n",
- "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
- "plt.ylabel('RMSE')\n",
- "plt.xlabel('Min Samples per Leaf')\n",
- "plt.show()"
+ "# Try feature selection\n",
+ "model.get_fscore\n",
+ "#selection = SelectFromModel(model, threshold = )"
]
}
],