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| a | b/Clinical Deterioration Prediction Model - Selection of Ensemble Algorithms .ipynb | ||
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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "markdown", |
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| 5 | "metadata": { |
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| 6 | "hide": true |
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| 7 | }, |
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| 8 | "source": [ |
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| 9 | "import numpy as np\n", |
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| 10 | "import os\n", |
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| 11 | "import pandas as pd\n", |
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| 12 | "from sklearn.model_selection import train_test_split\n", |
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| 13 | "from sklearn.ensemble import RandomForestClassifier\n", |
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| 14 | "# ^^^ pyforest auto-imports - don't write above this line\n", |
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| 15 | "import numpy as np\n", |
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| 16 | "import matplotlib.pyplot as plt\n", |
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| 17 | "import pandas as pd\n", |
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| 18 | "import os\n", |
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| 19 | "import sklearn\n", |
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| 20 | "import matplotlib.pyplot as plt\n", |
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| 21 | "import seaborn as sns\n", |
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| 22 | "import pandas as pd\n", |
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| 23 | "import os\n", |
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| 24 | "\n", |
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| 25 | "$$\n", |
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| 26 | "\\renewcommand{\\like}{{\\cal L}}\n", |
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| 27 | "\\renewcommand{\\loglike}{{\\ell}}\n", |
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| 28 | "\\renewcommand{\\err}{{\\cal E}}\n", |
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| 29 | "\\renewcommand{\\dat}{{\\cal D}}\n", |
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| 30 | "\\renewcommand{\\hyp}{{\\cal H}}\n", |
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| 31 | "\\renewcommand{\\Ex}[2]{E_{#1}[#2]}\n", |
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| 32 | "\\renewcommand{\\x}{{\\mathbf x}}\n", |
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| 33 | "\\renewcommand{\\v}[1]{{\\mathbf #1}}\n", |
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| 34 | "$$" |
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| 35 | ] |
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| 36 | }, |
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| 37 | { |
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| 38 | "cell_type": "markdown", |
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| 39 | "metadata": {}, |
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| 40 | "source": [ |
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| 41 | "# Clinical Deterioration Prediction Model - Selection of Ensemble Algorithms " |
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| 42 | ] |
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| 43 | }, |
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| 44 | { |
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| 45 | "cell_type": "markdown", |
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| 46 | "metadata": {}, |
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| 47 | "source": [ |
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| 48 | "## Data\n", |
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| 49 | "\n", |
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| 50 | "The final dataset used for the inferential statistics project includes unique ICU admission of 46,234 patients’ demographic (age), vital (blood pressure, heart rate, body temperature, and Glasgow Comma Scale), underlying conditions (HIV, metastatic cancer, and hematologic malignancy), admission type (scheduled surgical, medical, or unscheduled surgical), renal (urinary output, and Blood Urea Nitrogen), and others (serum bicarbonate level, sodium level, potassium level, and bilirubin level) data. This dataset is build based on the commonly used mortality prediction tool, Simplified Acute Physiology Score II (SAPSII). " |
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| 51 | ] |
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| 52 | }, |
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| 53 | { |
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| 54 | "cell_type": "code", |
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| 55 | "execution_count": 1, |
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| 56 | "metadata": {}, |
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| 57 | "outputs": [ |
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| 58 | { |
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| 59 | "data": { |
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| 60 | "application/javascript": [ |
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| 61 | "\n", |
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| 62 | " if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import os'); }\n", |
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| 63 | " " |
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| 64 | ], |
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| 65 | "text/plain": [ |
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| 66 | "<IPython.core.display.Javascript object>" |
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| 67 | ] |
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| 68 | }, |
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| 69 | "metadata": {}, |
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| 70 | "output_type": "display_data" |
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| 71 | }, |
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| 72 | { |
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| 73 | "data": { |
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| 74 | "text/plain": [ |
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| 75 | "'C:\\\\Users\\\\abebu\\\\Dropbox\\\\Data Science\\\\Projects\\\\Capstone Project 1\\\\Potential Projects\\\\9. MIMIC\\\\Machine Learning\\\\Clinical-Deterioration-Prediction-Model---Bayesian-Linear-Regression'" |
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| 76 | ] |
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| 77 | }, |
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| 78 | "execution_count": 1, |
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| 79 | "metadata": {}, |
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| 80 | "output_type": "execute_result" |
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| 81 | } |
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| 82 | ], |
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| 83 | "source": [ |
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| 84 | "os.getcwd()" |
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| 85 | ] |
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| 86 | }, |
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| 87 | { |
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| 88 | "cell_type": "code", |
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| 89 | "execution_count": 3, |
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| 90 | "metadata": {}, |
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| 91 | "outputs": [ |
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| 92 | { |
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| 93 | "data": { |
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| 94 | "application/javascript": [ |
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| 95 | "\n", |
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| 96 | " if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import os'); }\n", |
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| 97 | " " |
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| 98 | ], |
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| 99 | "text/plain": [ |
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| 100 | "<IPython.core.display.Javascript object>" |
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| 101 | ] |
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| 102 | }, |
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| 103 | "metadata": {}, |
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| 104 | "output_type": "display_data" |
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| 105 | }, |
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| 106 | { |
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| 107 | "data": { |
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| 108 | "application/javascript": [ |
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| 109 | "\n", |
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| 110 | " if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import os\\nimport pandas as pd'); }\n", |
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| 111 | " " |
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| 112 | ], |
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| 113 | "text/plain": [ |
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| 114 | "<IPython.core.display.Javascript object>" |
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| 115 | ] |
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| 116 | }, |
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| 117 | "metadata": {}, |
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| 118 | "output_type": "display_data" |
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| 119 | }, |
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| 120 | { |
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| 121 | "data": { |
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| 122 | "text/html": [ |
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| 123 | "<div>\n", |
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| 124 | "<style scoped>\n", |
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| 125 | " .dataframe tbody tr th:only-of-type {\n", |
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| 126 | " vertical-align: middle;\n", |
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| 127 | " }\n", |
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| 128 | "\n", |
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| 129 | " .dataframe tbody tr th {\n", |
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| 130 | " vertical-align: top;\n", |
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| 131 | " }\n", |
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| 132 | "\n", |
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| 133 | " .dataframe thead th {\n", |
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| 134 | " text-align: right;\n", |
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| 135 | " }\n", |
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| 136 | "</style>\n", |
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| 137 | "<table border=\"1\" class=\"dataframe\">\n", |
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| 138 | " <thead>\n", |
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| 139 | " <tr style=\"text-align: right;\">\n", |
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| 140 | " <th></th>\n", |
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| 141 | " <th>SUBJECT_ID</th>\n", |
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| 142 | " <th>HADM_ID</th>\n", |
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| 143 | " <th>ICUSTAY_ID</th>\n", |
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| 144 | " <th>los</th>\n", |
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| 145 | " <th>hdeath</th>\n", |
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| 146 | " <th>death</th>\n", |
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| 147 | " <th>admission</th>\n", |
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| 148 | " <th>ud</th>\n", |
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| 149 | " <th>bun</th>\n", |
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| 150 | " <th>Bicarbonate</th>\n", |
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| 151 | " <th>...</th>\n", |
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| 152 | " <th>WBC_3.0</th>\n", |
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| 153 | " <th>hr_0.0</th>\n", |
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| 154 | " <th>hr_2.0</th>\n", |
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| 155 | " <th>hr_4.0</th>\n", |
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| 156 | " <th>hr_7.0</th>\n", |
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| 157 | " <th>hr_11.0</th>\n", |
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| 158 | " <th>bp_0.0</th>\n", |
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| 159 | " <th>bp_2.0</th>\n", |
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| 160 | " <th>bp_5.0</th>\n", |
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| 161 | " <th>bp_13.0</th>\n", |
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| 162 | " </tr>\n", |
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| 163 | " </thead>\n", |
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| 164 | " <tbody>\n", |
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| 165 | " <tr>\n", |
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| 166 | " <td>0</td>\n", |
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| 170 | " <td>3.2490</td>\n", |
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| 190 | " <td>1</td>\n", |
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| 192 | " <td>106296</td>\n", |
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| 194 | " <td>3.2788</td>\n", |
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| 212 | " </tr>\n", |
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| 214 | " <td>2</td>\n", |
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| 215 | " <td>270</td>\n", |
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| 216 | " <td>188028</td>\n", |
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| 217 | " <td>220345</td>\n", |
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| 218 | " <td>2.8939</td>\n", |
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| 219 | " <td>0</td>\n", |
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| 266 | " <td>1.6202</td>\n", |
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| 284 | " </tr>\n", |
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| 285 | " </tbody>\n", |
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| 286 | "</table>\n", |
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| 287 | "<p>5 rows × 33 columns</p>\n", |
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| 288 | "</div>" |
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| 289 | ], |
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| 290 | "text/plain": [ |
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| 291 | " SUBJECT_ID HADM_ID ICUSTAY_ID los hdeath death admission ud \\\n", |
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| 292 | "0 268 110404 280836 3.2490 1 1 8 0.0 \n", |
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| 293 | "1 269 106296 206613 3.2788 0 0 8 17.0 \n", |
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| 294 | "2 270 188028 220345 2.8939 0 0 0 0.0 \n", |
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| 295 | "3 271 173727 249196 2.0600 0 0 8 0.0 \n", |
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| 296 | "4 272 164716 210407 1.6202 0 0 8 0.0 \n", |
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| 297 | "\n", |
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| 298 | " bun Bicarbonate ... WBC_3.0 hr_0.0 hr_2.0 hr_4.0 hr_7.0 hr_11.0 \\\n", |
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| 299 | "0 6.0 0.0 ... 0 0 0 0 0 1 \n", |
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| 300 | "1 0.0 0.0 ... 0 1 0 0 0 0 \n", |
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| 301 | "2 0.0 0.0 ... 0 0 0 0 0 1 \n", |
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| 302 | "3 0.0 0.0 ... 0 1 0 0 0 0 \n", |
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| 303 | "4 0.0 0.0 ... 0 1 0 0 0 0 \n", |
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| 304 | "\n", |
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| 305 | " bp_0.0 bp_2.0 bp_5.0 bp_13.0 \n", |
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| 306 | "0 0 0 0 1 \n", |
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| 307 | "1 0 0 1 0 \n", |
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| 308 | "2 0 0 0 1 \n", |
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| 309 | "3 1 0 0 0 \n", |
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| 310 | "4 0 0 1 0 \n", |
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| 311 | "\n", |
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| 312 | "[5 rows x 33 columns]" |
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| 313 | ] |
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| 314 | }, |
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| 315 | "execution_count": 3, |
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| 316 | "metadata": {}, |
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| 317 | "output_type": "execute_result" |
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| 318 | } |
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| 319 | ], |
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| 320 | "source": [ |
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| 321 | "os.chdir(\"C://Users/abebu/Google Drive/mimic-iii-clinical-database-1.4\")\n", |
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| 322 | "saps = pd.read_csv(\"saps_ts.csv\", header=0, index_col=0)\n", |
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| 323 | "saps.head()\n" |
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| 324 | ] |
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| 325 | }, |
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| 326 | { |
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| 327 | "cell_type": "code", |
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| 328 | "execution_count": 4, |
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| 329 | "metadata": {}, |
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| 330 | "outputs": [ |
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| 331 | { |
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| 332 | "data": { |
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| 333 | "text/html": [ |
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| 334 | "<div>\n", |
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| 335 | "<style scoped>\n", |
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| 336 | " .dataframe tbody tr th:only-of-type {\n", |
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| 337 | " vertical-align: middle;\n", |
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| 338 | " }\n", |
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| 339 | "\n", |
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| 340 | " .dataframe tbody tr th {\n", |
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| 341 | " vertical-align: top;\n", |
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| 342 | " }\n", |
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| 343 | "\n", |
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| 344 | " .dataframe thead th {\n", |
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| 345 | " text-align: right;\n", |
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| 346 | " }\n", |
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| 347 | "</style>\n", |
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| 348 | "<table border=\"1\" class=\"dataframe\">\n", |
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| 349 | " <thead>\n", |
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| 350 | " <tr style=\"text-align: right;\">\n", |
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| 351 | " <th></th>\n", |
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| 352 | " <th>hdeath</th>\n", |
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| 353 | " <th>admission</th>\n", |
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| 354 | " <th>ud</th>\n", |
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| 355 | " <th>bun</th>\n", |
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| 356 | " <th>Bicarbonate</th>\n", |
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| 357 | " <th>ventilation</th>\n", |
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| 358 | " <th>Temp</th>\n", |
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| 359 | " <th>Bilirubin</th>\n", |
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| 360 | " <th>gcs</th>\n", |
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| 361 | " <th>AGE</th>\n", |
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| 362 | " <th>...</th>\n", |
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| 363 | " <th>WBC_3.0</th>\n", |
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| 364 | " <th>hr_0.0</th>\n", |
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| 365 | " <th>hr_2.0</th>\n", |
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| 366 | " <th>hr_4.0</th>\n", |
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| 367 | " <th>hr_7.0</th>\n", |
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| 368 | " <th>hr_11.0</th>\n", |
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| 369 | " <th>bp_0.0</th>\n", |
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| 370 | " <th>bp_2.0</th>\n", |
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| 371 | " <th>bp_5.0</th>\n", |
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| 372 | " <th>bp_13.0</th>\n", |
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| 373 | " </tr>\n", |
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| 374 | " </thead>\n", |
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| 375 | " <tbody>\n", |
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| 376 | " <tr>\n", |
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| 438 | " <td>0</td>\n", |
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| 439 | " <td>0</td>\n", |
||
| 440 | " <td>0</td>\n", |
||
| 441 | " <td>0</td>\n", |
||
| 442 | " <td>1</td>\n", |
||
| 443 | " <td>0</td>\n", |
||
| 444 | " <td>0</td>\n", |
||
| 445 | " <td>0</td>\n", |
||
| 446 | " <td>1</td>\n", |
||
| 447 | " </tr>\n", |
||
| 448 | " <tr>\n", |
||
| 449 | " <td>3</td>\n", |
||
| 450 | " <td>0</td>\n", |
||
| 451 | " <td>8</td>\n", |
||
| 452 | " <td>0.0</td>\n", |
||
| 453 | " <td>0.0</td>\n", |
||
| 454 | " <td>0.0</td>\n", |
||
| 455 | " <td>6.0</td>\n", |
||
| 456 | " <td>3.0</td>\n", |
||
| 457 | " <td>0.0</td>\n", |
||
| 458 | " <td>0.0</td>\n", |
||
| 459 | " <td>7.0</td>\n", |
||
| 460 | " <td>...</td>\n", |
||
| 461 | " <td>0</td>\n", |
||
| 462 | " <td>1</td>\n", |
||
| 463 | " <td>0</td>\n", |
||
| 464 | " <td>0</td>\n", |
||
| 465 | " <td>0</td>\n", |
||
| 466 | " <td>0</td>\n", |
||
| 467 | " <td>1</td>\n", |
||
| 468 | " <td>0</td>\n", |
||
| 469 | " <td>0</td>\n", |
||
| 470 | " <td>0</td>\n", |
||
| 471 | " </tr>\n", |
||
| 472 | " <tr>\n", |
||
| 473 | " <td>4</td>\n", |
||
| 474 | " <td>0</td>\n", |
||
| 475 | " <td>8</td>\n", |
||
| 476 | " <td>0.0</td>\n", |
||
| 477 | " <td>0.0</td>\n", |
||
| 478 | " <td>0.0</td>\n", |
||
| 479 | " <td>0.0</td>\n", |
||
| 480 | " <td>3.0</td>\n", |
||
| 481 | " <td>0.0</td>\n", |
||
| 482 | " <td>0.0</td>\n", |
||
| 483 | " <td>12.0</td>\n", |
||
| 484 | " <td>...</td>\n", |
||
| 485 | " <td>0</td>\n", |
||
| 486 | " <td>1</td>\n", |
||
| 487 | " <td>0</td>\n", |
||
| 488 | " <td>0</td>\n", |
||
| 489 | " <td>0</td>\n", |
||
| 490 | " <td>0</td>\n", |
||
| 491 | " <td>0</td>\n", |
||
| 492 | " <td>0</td>\n", |
||
| 493 | " <td>1</td>\n", |
||
| 494 | " <td>0</td>\n", |
||
| 495 | " </tr>\n", |
||
| 496 | " <tr>\n", |
||
| 497 | " <td>...</td>\n", |
||
| 498 | " <td>...</td>\n", |
||
| 499 | " <td>...</td>\n", |
||
| 500 | " <td>...</td>\n", |
||
| 501 | " <td>...</td>\n", |
||
| 502 | " <td>...</td>\n", |
||
| 503 | " <td>...</td>\n", |
||
| 504 | " <td>...</td>\n", |
||
| 505 | " <td>...</td>\n", |
||
| 506 | " <td>...</td>\n", |
||
| 507 | " <td>...</td>\n", |
||
| 508 | " <td>...</td>\n", |
||
| 509 | " <td>...</td>\n", |
||
| 510 | " <td>...</td>\n", |
||
| 511 | " <td>...</td>\n", |
||
| 512 | " <td>...</td>\n", |
||
| 513 | " <td>...</td>\n", |
||
| 514 | " <td>...</td>\n", |
||
| 515 | " <td>...</td>\n", |
||
| 516 | " <td>...</td>\n", |
||
| 517 | " <td>...</td>\n", |
||
| 518 | " <td>...</td>\n", |
||
| 519 | " </tr>\n", |
||
| 520 | " <tr>\n", |
||
| 521 | " <td>61112</td>\n", |
||
| 522 | " <td>0</td>\n", |
||
| 523 | " <td>8</td>\n", |
||
| 524 | " <td>0.0</td>\n", |
||
| 525 | " <td>0.0</td>\n", |
||
| 526 | " <td>0.0</td>\n", |
||
| 527 | " <td>6.0</td>\n", |
||
| 528 | " <td>3.0</td>\n", |
||
| 529 | " <td>0.0</td>\n", |
||
| 530 | " <td>26.0</td>\n", |
||
| 531 | " <td>16.0</td>\n", |
||
| 532 | " <td>...</td>\n", |
||
| 533 | " <td>0</td>\n", |
||
| 534 | " <td>0</td>\n", |
||
| 535 | " <td>1</td>\n", |
||
| 536 | " <td>0</td>\n", |
||
| 537 | " <td>0</td>\n", |
||
| 538 | " <td>0</td>\n", |
||
| 539 | " <td>0</td>\n", |
||
| 540 | " <td>0</td>\n", |
||
| 541 | " <td>0</td>\n", |
||
| 542 | " <td>1</td>\n", |
||
| 543 | " </tr>\n", |
||
| 544 | " <tr>\n", |
||
| 545 | " <td>61113</td>\n", |
||
| 546 | " <td>0</td>\n", |
||
| 547 | " <td>8</td>\n", |
||
| 548 | " <td>0.0</td>\n", |
||
| 549 | " <td>0.0</td>\n", |
||
| 550 | " <td>0.0</td>\n", |
||
| 551 | " <td>0.0</td>\n", |
||
| 552 | " <td>3.0</td>\n", |
||
| 553 | " <td>0.0</td>\n", |
||
| 554 | " <td>5.0</td>\n", |
||
| 555 | " <td>18.0</td>\n", |
||
| 556 | " <td>...</td>\n", |
||
| 557 | " <td>0</td>\n", |
||
| 558 | " <td>0</td>\n", |
||
| 559 | " <td>1</td>\n", |
||
| 560 | " <td>0</td>\n", |
||
| 561 | " <td>0</td>\n", |
||
| 562 | " <td>0</td>\n", |
||
| 563 | " <td>1</td>\n", |
||
| 564 | " <td>0</td>\n", |
||
| 565 | " <td>0</td>\n", |
||
| 566 | " <td>0</td>\n", |
||
| 567 | " </tr>\n", |
||
| 568 | " <tr>\n", |
||
| 569 | " <td>61114</td>\n", |
||
| 570 | " <td>0</td>\n", |
||
| 571 | " <td>0</td>\n", |
||
| 572 | " <td>0.0</td>\n", |
||
| 573 | " <td>0.0</td>\n", |
||
| 574 | " <td>0.0</td>\n", |
||
| 575 | " <td>6.0</td>\n", |
||
| 576 | " <td>3.0</td>\n", |
||
| 577 | " <td>0.0</td>\n", |
||
| 578 | " <td>7.0</td>\n", |
||
| 579 | " <td>7.0</td>\n", |
||
| 580 | " <td>...</td>\n", |
||
| 581 | " <td>0</td>\n", |
||
| 582 | " <td>1</td>\n", |
||
| 583 | " <td>0</td>\n", |
||
| 584 | " <td>0</td>\n", |
||
| 585 | " <td>0</td>\n", |
||
| 586 | " <td>0</td>\n", |
||
| 587 | " <td>0</td>\n", |
||
| 588 | " <td>0</td>\n", |
||
| 589 | " <td>1</td>\n", |
||
| 590 | " <td>0</td>\n", |
||
| 591 | " </tr>\n", |
||
| 592 | " <tr>\n", |
||
| 593 | " <td>61115</td>\n", |
||
| 594 | " <td>0</td>\n", |
||
| 595 | " <td>0</td>\n", |
||
| 596 | " <td>0.0</td>\n", |
||
| 597 | " <td>0.0</td>\n", |
||
| 598 | " <td>0.0</td>\n", |
||
| 599 | " <td>6.0</td>\n", |
||
| 600 | " <td>3.0</td>\n", |
||
| 601 | " <td>0.0</td>\n", |
||
| 602 | " <td>26.0</td>\n", |
||
| 603 | " <td>12.0</td>\n", |
||
| 604 | " <td>...</td>\n", |
||
| 605 | " <td>0</td>\n", |
||
| 606 | " <td>1</td>\n", |
||
| 607 | " <td>0</td>\n", |
||
| 608 | " <td>0</td>\n", |
||
| 609 | " <td>0</td>\n", |
||
| 610 | " <td>0</td>\n", |
||
| 611 | " <td>0</td>\n", |
||
| 612 | " <td>0</td>\n", |
||
| 613 | " <td>0</td>\n", |
||
| 614 | " <td>1</td>\n", |
||
| 615 | " </tr>\n", |
||
| 616 | " <tr>\n", |
||
| 617 | " <td>61116</td>\n", |
||
| 618 | " <td>0</td>\n", |
||
| 619 | " <td>8</td>\n", |
||
| 620 | " <td>0.0</td>\n", |
||
| 621 | " <td>0.0</td>\n", |
||
| 622 | " <td>0.0</td>\n", |
||
| 623 | " <td>11.0</td>\n", |
||
| 624 | " <td>3.0</td>\n", |
||
| 625 | " <td>0.0</td>\n", |
||
| 626 | " <td>26.0</td>\n", |
||
| 627 | " <td>0.0</td>\n", |
||
| 628 | " <td>...</td>\n", |
||
| 629 | " <td>0</td>\n", |
||
| 630 | " <td>1</td>\n", |
||
| 631 | " <td>0</td>\n", |
||
| 632 | " <td>0</td>\n", |
||
| 633 | " <td>0</td>\n", |
||
| 634 | " <td>0</td>\n", |
||
| 635 | " <td>0</td>\n", |
||
| 636 | " <td>0</td>\n", |
||
| 637 | " <td>1</td>\n", |
||
| 638 | " <td>0</td>\n", |
||
| 639 | " </tr>\n", |
||
| 640 | " </tbody>\n", |
||
| 641 | "</table>\n", |
||
| 642 | "<p>61117 rows × 28 columns</p>\n", |
||
| 643 | "</div>" |
||
| 644 | ], |
||
| 645 | "text/plain": [ |
||
| 646 | " hdeath admission ud bun Bicarbonate ventilation Temp \\\n", |
||
| 647 | "0 1 8 0.0 6.0 0.0 6.0 0.0 \n", |
||
| 648 | "1 0 8 17.0 0.0 0.0 0.0 0.0 \n", |
||
| 649 | "2 0 0 0.0 0.0 0.0 0.0 3.0 \n", |
||
| 650 | "3 0 8 0.0 0.0 0.0 6.0 3.0 \n", |
||
| 651 | "4 0 8 0.0 0.0 0.0 0.0 3.0 \n", |
||
| 652 | "... ... ... ... ... ... ... ... \n", |
||
| 653 | "61112 0 8 0.0 0.0 0.0 6.0 3.0 \n", |
||
| 654 | "61113 0 8 0.0 0.0 0.0 0.0 3.0 \n", |
||
| 655 | "61114 0 0 0.0 0.0 0.0 6.0 3.0 \n", |
||
| 656 | "61115 0 0 0.0 0.0 0.0 6.0 3.0 \n", |
||
| 657 | "61116 0 8 0.0 0.0 0.0 11.0 3.0 \n", |
||
| 658 | "\n", |
||
| 659 | " Bilirubin gcs AGE ... WBC_3.0 hr_0.0 hr_2.0 hr_4.0 hr_7.0 \\\n", |
||
| 660 | "0 0.0 26.0 12.0 ... 0 0 0 0 0 \n", |
||
| 661 | "1 0.0 0.0 7.0 ... 0 1 0 0 0 \n", |
||
| 662 | "2 0.0 0.0 18.0 ... 0 0 0 0 0 \n", |
||
| 663 | "3 0.0 0.0 7.0 ... 0 1 0 0 0 \n", |
||
| 664 | "4 0.0 0.0 12.0 ... 0 1 0 0 0 \n", |
||
| 665 | "... ... ... ... ... ... ... ... ... ... \n", |
||
| 666 | "61112 0.0 26.0 16.0 ... 0 0 1 0 0 \n", |
||
| 667 | "61113 0.0 5.0 18.0 ... 0 0 1 0 0 \n", |
||
| 668 | "61114 0.0 7.0 7.0 ... 0 1 0 0 0 \n", |
||
| 669 | "61115 0.0 26.0 12.0 ... 0 1 0 0 0 \n", |
||
| 670 | "61116 0.0 26.0 0.0 ... 0 1 0 0 0 \n", |
||
| 671 | "\n", |
||
| 672 | " hr_11.0 bp_0.0 bp_2.0 bp_5.0 bp_13.0 \n", |
||
| 673 | "0 1 0 0 0 1 \n", |
||
| 674 | "1 0 0 0 1 0 \n", |
||
| 675 | "2 1 0 0 0 1 \n", |
||
| 676 | "3 0 1 0 0 0 \n", |
||
| 677 | "4 0 0 0 1 0 \n", |
||
| 678 | "... ... ... ... ... ... \n", |
||
| 679 | "61112 0 0 0 0 1 \n", |
||
| 680 | "61113 0 1 0 0 0 \n", |
||
| 681 | "61114 0 0 0 1 0 \n", |
||
| 682 | "61115 0 0 0 0 1 \n", |
||
| 683 | "61116 0 0 0 1 0 \n", |
||
| 684 | "\n", |
||
| 685 | "[61117 rows x 28 columns]" |
||
| 686 | ] |
||
| 687 | }, |
||
| 688 | "execution_count": 4, |
||
| 689 | "metadata": {}, |
||
| 690 | "output_type": "execute_result" |
||
| 691 | } |
||
| 692 | ], |
||
| 693 | "source": [ |
||
| 694 | "saps_e=saps.drop(['los','death','SUBJECT_ID','HADM_ID', 'ICUSTAY_ID'], axis=1)\n", |
||
| 695 | "saps_e" |
||
| 696 | ] |
||
| 697 | }, |
||
| 698 | { |
||
| 699 | "cell_type": "code", |
||
| 700 | "execution_count": 12, |
||
| 701 | "metadata": {}, |
||
| 702 | "outputs": [], |
||
| 703 | "source": [ |
||
| 704 | "# Pandas and numpy for data manipulation\n", |
||
| 705 | "import pandas as pd\n", |
||
| 706 | "import numpy as np\n", |
||
| 707 | "np.random.seed(42)\n", |
||
| 708 | "\n", |
||
| 709 | " \n", |
||
| 710 | "# Matplotlib and seaborn for plotting\n", |
||
| 711 | "import matplotlib.pyplot as plt\n", |
||
| 712 | "%matplotlib inline\n", |
||
| 713 | "\n", |
||
| 714 | "import matplotlib\n", |
||
| 715 | "matplotlib.rcParams['font.size'] = 16\n", |
||
| 716 | "matplotlib.rcParams['figure.figsize'] = (9, 9)\n", |
||
| 717 | "\n", |
||
| 718 | "import seaborn as sns\n", |
||
| 719 | "\n", |
||
| 720 | "# Scipy helper functions\n", |
||
| 721 | "from scipy.stats import percentileofscore\n", |
||
| 722 | "from scipy import stats" |
||
| 723 | ] |
||
| 724 | }, |
||
| 725 | { |
||
| 726 | "cell_type": "code", |
||
| 727 | "execution_count": 10, |
||
| 728 | "metadata": {}, |
||
| 729 | "outputs": [], |
||
| 730 | "source": [ |
||
| 731 | "# Standard ensembel ML Models for comparison\n", |
||
| 732 | "\n", |
||
| 733 | "from sklearn.ensemble import RandomForestClassifier\n", |
||
| 734 | "from sklearn.ensemble import ExtraTreesClassifier\n", |
||
| 735 | "from sklearn.ensemble import GradientBoostingClassifier\n", |
||
| 736 | "\n", |
||
| 737 | "# Splitting data into training/testing\n", |
||
| 738 | "from sklearn.model_selection import train_test_split\n", |
||
| 739 | "from sklearn.preprocessing import MinMaxScaler\n", |
||
| 740 | "\n", |
||
| 741 | "# Metrics\n", |
||
| 742 | "from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error\n", |
||
| 743 | "\n", |
||
| 744 | "# Distributions\n", |
||
| 745 | "import scipy" |
||
| 746 | ] |
||
| 747 | }, |
||
| 748 | { |
||
| 749 | "cell_type": "code", |
||
| 750 | "execution_count": 14, |
||
| 751 | "metadata": {}, |
||
| 752 | "outputs": [], |
||
| 753 | "source": [ |
||
| 754 | "\n", |
||
| 755 | "# PyMC3 for Bayesian Inference\n", |
||
| 756 | "import pymc3 as pm" |
||
| 757 | ] |
||
| 758 | }, |
||
| 759 | { |
||
| 760 | "cell_type": "code", |
||
| 761 | "execution_count": 5, |
||
| 762 | "metadata": {}, |
||
| 763 | "outputs": [], |
||
| 764 | "source": [ |
||
| 765 | "\n", |
||
| 766 | "# grade and returns training and testing datasets\n", |
||
| 767 | "def format_data(df):\n", |
||
| 768 | " # Target is hospital death\n", |
||
| 769 | " labels = saps_e['hdeath']\n", |
||
| 770 | " \n", |
||
| 771 | " # Drop target (hdeath) from features\n", |
||
| 772 | " df = df.drop(columns=['hdeath'])\n", |
||
| 773 | " \n", |
||
| 774 | " # Split into training/testing sets with 30% split\n", |
||
| 775 | " X_train, X_test, y_train, y_test = train_test_split(df, labels, \n", |
||
| 776 | " test_size = 0.30,\n", |
||
| 777 | " random_state=42)\n", |
||
| 778 | " \n", |
||
| 779 | " return X_train, X_test, y_train, y_test\n" |
||
| 780 | ] |
||
| 781 | }, |
||
| 782 | { |
||
| 783 | "cell_type": "code", |
||
| 784 | "execution_count": 6, |
||
| 785 | "metadata": {}, |
||
| 786 | "outputs": [ |
||
| 787 | { |
||
| 788 | "data": { |
||
| 789 | "application/javascript": [ |
||
| 790 | "\n", |
||
| 791 | " if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import os\\nimport pandas as pd\\nfrom sklearn.model_selection import train_test_split'); }\n", |
||
| 792 | " " |
||
| 793 | ], |
||
| 794 | "text/plain": [ |
||
| 795 | "<IPython.core.display.Javascript object>" |
||
| 796 | ] |
||
| 797 | }, |
||
| 798 | "metadata": {}, |
||
| 799 | "output_type": "display_data" |
||
| 800 | }, |
||
| 801 | { |
||
| 802 | "data": { |
||
| 803 | "text/html": [ |
||
| 804 | "<div>\n", |
||
| 805 | "<style scoped>\n", |
||
| 806 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 807 | " vertical-align: middle;\n", |
||
| 808 | " }\n", |
||
| 809 | "\n", |
||
| 810 | " .dataframe tbody tr th {\n", |
||
| 811 | " vertical-align: top;\n", |
||
| 812 | " }\n", |
||
| 813 | "\n", |
||
| 814 | " .dataframe thead th {\n", |
||
| 815 | " text-align: right;\n", |
||
| 816 | " }\n", |
||
| 817 | "</style>\n", |
||
| 818 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 819 | " <thead>\n", |
||
| 820 | " <tr style=\"text-align: right;\">\n", |
||
| 821 | " <th></th>\n", |
||
| 822 | " <th>admission</th>\n", |
||
| 823 | " <th>ud</th>\n", |
||
| 824 | " <th>bun</th>\n", |
||
| 825 | " <th>Bicarbonate</th>\n", |
||
| 826 | " <th>ventilation</th>\n", |
||
| 827 | " <th>Temp</th>\n", |
||
| 828 | " <th>Bilirubin</th>\n", |
||
| 829 | " <th>gcs</th>\n", |
||
| 830 | " <th>AGE</th>\n", |
||
| 831 | " <th>UO</th>\n", |
||
| 832 | " <th>...</th>\n", |
||
| 833 | " <th>WBC_3.0</th>\n", |
||
| 834 | " <th>hr_0.0</th>\n", |
||
| 835 | " <th>hr_2.0</th>\n", |
||
| 836 | " <th>hr_4.0</th>\n", |
||
| 837 | " <th>hr_7.0</th>\n", |
||
| 838 | " <th>hr_11.0</th>\n", |
||
| 839 | " <th>bp_0.0</th>\n", |
||
| 840 | " <th>bp_2.0</th>\n", |
||
| 841 | " <th>bp_5.0</th>\n", |
||
| 842 | " <th>bp_13.0</th>\n", |
||
| 843 | " </tr>\n", |
||
| 844 | " </thead>\n", |
||
| 845 | " <tbody>\n", |
||
| 846 | " <tr>\n", |
||
| 847 | " <td>53545</td>\n", |
||
| 848 | " <td>8</td>\n", |
||
| 849 | " <td>0.0</td>\n", |
||
| 850 | " <td>0.0</td>\n", |
||
| 851 | " <td>0.0</td>\n", |
||
| 852 | " <td>0.0</td>\n", |
||
| 853 | " <td>3.0</td>\n", |
||
| 854 | " <td>0.0</td>\n", |
||
| 855 | " <td>0.0</td>\n", |
||
| 856 | " <td>15.0</td>\n", |
||
| 857 | " <td>0.0</td>\n", |
||
| 858 | " <td>...</td>\n", |
||
| 859 | " <td>0</td>\n", |
||
| 860 | " <td>1</td>\n", |
||
| 861 | " <td>0</td>\n", |
||
| 862 | " <td>0</td>\n", |
||
| 863 | " <td>0</td>\n", |
||
| 864 | " <td>0</td>\n", |
||
| 865 | " <td>1</td>\n", |
||
| 866 | " <td>0</td>\n", |
||
| 867 | " <td>0</td>\n", |
||
| 868 | " <td>0</td>\n", |
||
| 869 | " </tr>\n", |
||
| 870 | " <tr>\n", |
||
| 871 | " <td>51512</td>\n", |
||
| 872 | " <td>8</td>\n", |
||
| 873 | " <td>0.0</td>\n", |
||
| 874 | " <td>6.0</td>\n", |
||
| 875 | " <td>0.0</td>\n", |
||
| 876 | " <td>0.0</td>\n", |
||
| 877 | " <td>3.0</td>\n", |
||
| 878 | " <td>0.0</td>\n", |
||
| 879 | " <td>0.0</td>\n", |
||
| 880 | " <td>18.0</td>\n", |
||
| 881 | " <td>0.0</td>\n", |
||
| 882 | " <td>...</td>\n", |
||
| 883 | " <td>0</td>\n", |
||
| 884 | " <td>0</td>\n", |
||
| 885 | " <td>1</td>\n", |
||
| 886 | " <td>0</td>\n", |
||
| 887 | " <td>0</td>\n", |
||
| 888 | " <td>0</td>\n", |
||
| 889 | " <td>0</td>\n", |
||
| 890 | " <td>0</td>\n", |
||
| 891 | " <td>1</td>\n", |
||
| 892 | " <td>0</td>\n", |
||
| 893 | " </tr>\n", |
||
| 894 | " <tr>\n", |
||
| 895 | " <td>23837</td>\n", |
||
| 896 | " <td>8</td>\n", |
||
| 897 | " <td>0.0</td>\n", |
||
| 898 | " <td>0.0</td>\n", |
||
| 899 | " <td>0.0</td>\n", |
||
| 900 | " <td>0.0</td>\n", |
||
| 901 | " <td>3.0</td>\n", |
||
| 902 | " <td>0.0</td>\n", |
||
| 903 | " <td>0.0</td>\n", |
||
| 904 | " <td>12.0</td>\n", |
||
| 905 | " <td>0.0</td>\n", |
||
| 906 | " <td>...</td>\n", |
||
| 907 | " <td>0</td>\n", |
||
| 908 | " <td>0</td>\n", |
||
| 909 | " <td>1</td>\n", |
||
| 910 | " <td>0</td>\n", |
||
| 911 | " <td>0</td>\n", |
||
| 912 | " <td>0</td>\n", |
||
| 913 | " <td>0</td>\n", |
||
| 914 | " <td>0</td>\n", |
||
| 915 | " <td>1</td>\n", |
||
| 916 | " <td>0</td>\n", |
||
| 917 | " </tr>\n", |
||
| 918 | " <tr>\n", |
||
| 919 | " <td>21929</td>\n", |
||
| 920 | " <td>0</td>\n", |
||
| 921 | " <td>0.0</td>\n", |
||
| 922 | " <td>10.0</td>\n", |
||
| 923 | " <td>0.0</td>\n", |
||
| 924 | " <td>6.0</td>\n", |
||
| 925 | " <td>3.0</td>\n", |
||
| 926 | " <td>0.0</td>\n", |
||
| 927 | " <td>5.0</td>\n", |
||
| 928 | " <td>18.0</td>\n", |
||
| 929 | " <td>0.0</td>\n", |
||
| 930 | " <td>...</td>\n", |
||
| 931 | " <td>0</td>\n", |
||
| 932 | " <td>1</td>\n", |
||
| 933 | " <td>0</td>\n", |
||
| 934 | " <td>0</td>\n", |
||
| 935 | " <td>0</td>\n", |
||
| 936 | " <td>0</td>\n", |
||
| 937 | " <td>0</td>\n", |
||
| 938 | " <td>0</td>\n", |
||
| 939 | " <td>0</td>\n", |
||
| 940 | " <td>1</td>\n", |
||
| 941 | " </tr>\n", |
||
| 942 | " <tr>\n", |
||
| 943 | " <td>57339</td>\n", |
||
| 944 | " <td>8</td>\n", |
||
| 945 | " <td>0.0</td>\n", |
||
| 946 | " <td>0.0</td>\n", |
||
| 947 | " <td>0.0</td>\n", |
||
| 948 | " <td>0.0</td>\n", |
||
| 949 | " <td>3.0</td>\n", |
||
| 950 | " <td>0.0</td>\n", |
||
| 951 | " <td>0.0</td>\n", |
||
| 952 | " <td>12.0</td>\n", |
||
| 953 | " <td>0.0</td>\n", |
||
| 954 | " <td>...</td>\n", |
||
| 955 | " <td>0</td>\n", |
||
| 956 | " <td>0</td>\n", |
||
| 957 | " <td>1</td>\n", |
||
| 958 | " <td>0</td>\n", |
||
| 959 | " <td>0</td>\n", |
||
| 960 | " <td>0</td>\n", |
||
| 961 | " <td>0</td>\n", |
||
| 962 | " <td>0</td>\n", |
||
| 963 | " <td>1</td>\n", |
||
| 964 | " <td>0</td>\n", |
||
| 965 | " </tr>\n", |
||
| 966 | " </tbody>\n", |
||
| 967 | "</table>\n", |
||
| 968 | "<p>5 rows × 27 columns</p>\n", |
||
| 969 | "</div>" |
||
| 970 | ], |
||
| 971 | "text/plain": [ |
||
| 972 | " admission ud bun Bicarbonate ventilation Temp Bilirubin gcs \\\n", |
||
| 973 | "53545 8 0.0 0.0 0.0 0.0 3.0 0.0 0.0 \n", |
||
| 974 | "51512 8 0.0 6.0 0.0 0.0 3.0 0.0 0.0 \n", |
||
| 975 | "23837 8 0.0 0.0 0.0 0.0 3.0 0.0 0.0 \n", |
||
| 976 | "21929 0 0.0 10.0 0.0 6.0 3.0 0.0 5.0 \n", |
||
| 977 | "57339 8 0.0 0.0 0.0 0.0 3.0 0.0 0.0 \n", |
||
| 978 | "\n", |
||
| 979 | " AGE UO ... WBC_3.0 hr_0.0 hr_2.0 hr_4.0 hr_7.0 hr_11.0 \\\n", |
||
| 980 | "53545 15.0 0.0 ... 0 1 0 0 0 0 \n", |
||
| 981 | "51512 18.0 0.0 ... 0 0 1 0 0 0 \n", |
||
| 982 | "23837 12.0 0.0 ... 0 0 1 0 0 0 \n", |
||
| 983 | "21929 18.0 0.0 ... 0 1 0 0 0 0 \n", |
||
| 984 | "57339 12.0 0.0 ... 0 0 1 0 0 0 \n", |
||
| 985 | "\n", |
||
| 986 | " bp_0.0 bp_2.0 bp_5.0 bp_13.0 \n", |
||
| 987 | "53545 1 0 0 0 \n", |
||
| 988 | "51512 0 0 1 0 \n", |
||
| 989 | "23837 0 0 1 0 \n", |
||
| 990 | "21929 0 0 0 1 \n", |
||
| 991 | "57339 0 0 1 0 \n", |
||
| 992 | "\n", |
||
| 993 | "[5 rows x 27 columns]" |
||
| 994 | ] |
||
| 995 | }, |
||
| 996 | "execution_count": 6, |
||
| 997 | "metadata": {}, |
||
| 998 | "output_type": "execute_result" |
||
| 999 | } |
||
| 1000 | ], |
||
| 1001 | "source": [ |
||
| 1002 | "X_train, X_test, y_train, y_test = format_data(saps_e)\n", |
||
| 1003 | "X_train.head()\n" |
||
| 1004 | ] |
||
| 1005 | }, |
||
| 1006 | { |
||
| 1007 | "cell_type": "code", |
||
| 1008 | "execution_count": 7, |
||
| 1009 | "metadata": {}, |
||
| 1010 | "outputs": [ |
||
| 1011 | { |
||
| 1012 | "name": "stdout", |
||
| 1013 | "output_type": "stream", |
||
| 1014 | "text": [ |
||
| 1015 | "(42781, 27)\n", |
||
| 1016 | "(18336, 27)\n" |
||
| 1017 | ] |
||
| 1018 | } |
||
| 1019 | ], |
||
| 1020 | "source": [ |
||
| 1021 | "print(X_train.shape)\n", |
||
| 1022 | "print(X_test.shape)" |
||
| 1023 | ] |
||
| 1024 | }, |
||
| 1025 | { |
||
| 1026 | "cell_type": "markdown", |
||
| 1027 | "metadata": {}, |
||
| 1028 | "source": [ |
||
| 1029 | "### Standard Ensemble Machine Learning Models" |
||
| 1030 | ] |
||
| 1031 | }, |
||
| 1032 | { |
||
| 1033 | "cell_type": "code", |
||
| 1034 | "execution_count": 13, |
||
| 1035 | "metadata": {}, |
||
| 1036 | "outputs": [], |
||
| 1037 | "source": [ |
||
| 1038 | "# Evaluate several ml models by training on training set and testing on testing set\n", |
||
| 1039 | "def evaluate(X_train, X_test, y_train, y_test):\n", |
||
| 1040 | " #Names of models\n", |
||
| 1041 | " model_name_list = ['Random Forest', 'Extra Trees',\n", |
||
| 1042 | " 'Gradient Boosted']\n", |
||
| 1043 | " model = np.arange(1, 4)\n", |
||
| 1044 | " train_accuracy = np.empty(len(model))\n", |
||
| 1045 | " test_accuracy = np.empty(len(model))\n", |
||
| 1046 | " # Instantiate the models\n", |
||
| 1047 | " model1 = RandomForestClassifier(n_estimators=100)\n", |
||
| 1048 | " model2 = ExtraTreesClassifier(n_estimators=100)\n", |
||
| 1049 | " model3 = GradientBoostingClassifier(n_estimators=100)\n", |
||
| 1050 | " results =pd.DataFrame(columns=['train_accuracy', 'test_accuracy']) \n", |
||
| 1051 | " \n", |
||
| 1052 | " # Train and predict with each model\n", |
||
| 1053 | " for i, model in enumerate([model1, model2, model3]):\n", |
||
| 1054 | " model.fit(X_train, y_train)\n", |
||
| 1055 | " \n", |
||
| 1056 | " train_accuracy=model.score(X_train, y_train)\n", |
||
| 1057 | " test_accuracy=model.score(X_test, y_test)\n", |
||
| 1058 | " \n", |
||
| 1059 | " model_name=model_name_list[i]\n", |
||
| 1060 | " results.loc[model_name, :] =[train_accuracy, test_accuracy]\n", |
||
| 1061 | " return results\n", |
||
| 1062 | " " |
||
| 1063 | ] |
||
| 1064 | }, |
||
| 1065 | { |
||
| 1066 | "cell_type": "code", |
||
| 1067 | "execution_count": 14, |
||
| 1068 | "metadata": {}, |
||
| 1069 | "outputs": [ |
||
| 1070 | { |
||
| 1071 | "data": { |
||
| 1072 | "application/javascript": [ |
||
| 1073 | "\n", |
||
| 1074 | " if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport os\\nimport pandas as pd\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.ensemble import RandomForestClassifier'); }\n", |
||
| 1075 | " " |
||
| 1076 | ], |
||
| 1077 | "text/plain": [ |
||
| 1078 | "<IPython.core.display.Javascript object>" |
||
| 1079 | ] |
||
| 1080 | }, |
||
| 1081 | "metadata": {}, |
||
| 1082 | "output_type": "display_data" |
||
| 1083 | }, |
||
| 1084 | { |
||
| 1085 | "data": { |
||
| 1086 | "application/javascript": [ |
||
| 1087 | "\n", |
||
| 1088 | " if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport os\\nimport pandas as pd\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.ensemble import RandomForestClassifier'); }\n", |
||
| 1089 | " " |
||
| 1090 | ], |
||
| 1091 | "text/plain": [ |
||
| 1092 | "<IPython.core.display.Javascript object>" |
||
| 1093 | ] |
||
| 1094 | }, |
||
| 1095 | "metadata": {}, |
||
| 1096 | "output_type": "display_data" |
||
| 1097 | }, |
||
| 1098 | { |
||
| 1099 | "data": { |
||
| 1100 | "application/javascript": [ |
||
| 1101 | "\n", |
||
| 1102 | " if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport os\\nimport pandas as pd\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.ensemble import RandomForestClassifier'); }\n", |
||
| 1103 | " " |
||
| 1104 | ], |
||
| 1105 | "text/plain": [ |
||
| 1106 | "<IPython.core.display.Javascript object>" |
||
| 1107 | ] |
||
| 1108 | }, |
||
| 1109 | "metadata": {}, |
||
| 1110 | "output_type": "display_data" |
||
| 1111 | }, |
||
| 1112 | { |
||
| 1113 | "data": { |
||
| 1114 | "application/javascript": [ |
||
| 1115 | "\n", |
||
| 1116 | " if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport os\\nimport pandas as pd\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.ensemble import RandomForestClassifier'); }\n", |
||
| 1117 | " " |
||
| 1118 | ], |
||
| 1119 | "text/plain": [ |
||
| 1120 | "<IPython.core.display.Javascript object>" |
||
| 1121 | ] |
||
| 1122 | }, |
||
| 1123 | "metadata": {}, |
||
| 1124 | "output_type": "display_data" |
||
| 1125 | }, |
||
| 1126 | { |
||
| 1127 | "data": { |
||
| 1128 | "text/html": [ |
||
| 1129 | "<div>\n", |
||
| 1130 | "<style scoped>\n", |
||
| 1131 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 1132 | " vertical-align: middle;\n", |
||
| 1133 | " }\n", |
||
| 1134 | "\n", |
||
| 1135 | " .dataframe tbody tr th {\n", |
||
| 1136 | " vertical-align: top;\n", |
||
| 1137 | " }\n", |
||
| 1138 | "\n", |
||
| 1139 | " .dataframe thead th {\n", |
||
| 1140 | " text-align: right;\n", |
||
| 1141 | " }\n", |
||
| 1142 | "</style>\n", |
||
| 1143 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 1144 | " <thead>\n", |
||
| 1145 | " <tr style=\"text-align: right;\">\n", |
||
| 1146 | " <th></th>\n", |
||
| 1147 | " <th>train_accuracy</th>\n", |
||
| 1148 | " <th>test_accuracy</th>\n", |
||
| 1149 | " </tr>\n", |
||
| 1150 | " </thead>\n", |
||
| 1151 | " <tbody>\n", |
||
| 1152 | " <tr>\n", |
||
| 1153 | " <td>Random Forest</td>\n", |
||
| 1154 | " <td>0.949323</td>\n", |
||
| 1155 | " <td>0.91983</td>\n", |
||
| 1156 | " </tr>\n", |
||
| 1157 | " <tr>\n", |
||
| 1158 | " <td>Extra Trees</td>\n", |
||
| 1159 | " <td>0.949323</td>\n", |
||
| 1160 | " <td>0.919339</td>\n", |
||
| 1161 | " </tr>\n", |
||
| 1162 | " <tr>\n", |
||
| 1163 | " <td>Gradient Boosted</td>\n", |
||
| 1164 | " <td>0.924149</td>\n", |
||
| 1165 | " <td>0.925993</td>\n", |
||
| 1166 | " </tr>\n", |
||
| 1167 | " </tbody>\n", |
||
| 1168 | "</table>\n", |
||
| 1169 | "</div>" |
||
| 1170 | ], |
||
| 1171 | "text/plain": [ |
||
| 1172 | " train_accuracy test_accuracy\n", |
||
| 1173 | "Random Forest 0.949323 0.91983\n", |
||
| 1174 | "Extra Trees 0.949323 0.919339\n", |
||
| 1175 | "Gradient Boosted 0.924149 0.925993" |
||
| 1176 | ] |
||
| 1177 | }, |
||
| 1178 | "execution_count": 14, |
||
| 1179 | "metadata": {}, |
||
| 1180 | "output_type": "execute_result" |
||
| 1181 | } |
||
| 1182 | ], |
||
| 1183 | "source": [ |
||
| 1184 | "results = evaluate(X_train, X_test, y_train, y_test)\n", |
||
| 1185 | "results" |
||
| 1186 | ] |
||
| 1187 | }, |
||
| 1188 | { |
||
| 1189 | "cell_type": "code", |
||
| 1190 | "execution_count": null, |
||
| 1191 | "metadata": {}, |
||
| 1192 | "outputs": [], |
||
| 1193 | "source": [] |
||
| 1194 | } |
||
| 1195 | ], |
||
| 1196 | "metadata": { |
||
| 1197 | "kernelspec": { |
||
| 1198 | "display_name": "Python 3", |
||
| 1199 | "language": "python", |
||
| 1200 | "name": "python3" |
||
| 1201 | }, |
||
| 1202 | "language_info": { |
||
| 1203 | "codemirror_mode": { |
||
| 1204 | "name": "ipython", |
||
| 1205 | "version": 3 |
||
| 1206 | }, |
||
| 1207 | "file_extension": ".py", |
||
| 1208 | "mimetype": "text/x-python", |
||
| 1209 | "name": "python", |
||
| 1210 | "nbconvert_exporter": "python", |
||
| 1211 | "pygments_lexer": "ipython3", |
||
| 1212 | "version": "3.7.4" |
||
| 1213 | } |
||
| 1214 | }, |
||
| 1215 | "nbformat": 4, |
||
| 1216 | "nbformat_minor": 1 |
||
| 1217 | } |