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Noah L. Schrick 2023-04-21 20:36:43 -05:00
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Learning Practice 10 for the University of Tulsa's QM-7063 Data Mining Course\n",
"# Evaluating Predictive Performance\n",
"# Professor: Dr. Abdulrashid, Spring 2023\n",
"# Noah L. Schrick - 1492657"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Problem 5.7 \n",
"Table 5.7 shows a small set of predictive model validation results for a classification\n",
"model, with both actual values and propensities.\n",
"\n",
"| Propensity of 1 | Actual |\n",
"|-----------------|-----------------|\n",
"| 0.03 | 0 |\n",
"| 0.52 | 0 |\n",
"| 0.38 | 0 |\n",
"| 0.82 | 1 |\n",
"| 0.33 | 0 |\n",
"| 0.42 | 0 |\n",
"| 0.55 | 1 |\n",
"| 0.59 | 0 |\n",
"| 0.09 | 0 |\n",
"| 0.21 | 0 |\n",
"| 0.43 | 0 |\n",
"| 0.04 | 0 |\n",
"| 0.08 | 0 |\n",
"| 0.13 | 0 |\n",
"| 0.01 | 0 |\n",
"| 0.79 | 1 |\n",
"| 0.42 | 0 |\n",
"| 0.29 | 0 |\n",
"| 0.08 | 0 |\n",
"| 0.02 | 0 |"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# a.\n",
"Calculate error rates, sensitivity, and specificity using cutoffs of 0.25, 0.5, and 0.75."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Propensity of 1 Actual\n",
"0 0.03 0\n",
"1 0.52 0\n",
"2 0.38 0\n",
"3 0.82 1\n",
"4 0.33 0\n",
"5 0.42 0\n",
"6 0.55 1\n",
"7 0.59 0\n",
"8 0.09 0\n",
"9 0.21 0\n",
"10 0.43 0\n",
"11 0.04 0\n",
"12 0.08 0\n",
"13 0.13 0\n",
"14 0.01 0\n",
"15 0.79 1\n",
"16 0.42 0\n",
"17 0.29 0\n",
"18 0.08 0\n",
"19 0.02 0\n"
]
}
],
"source": [
"data = [\n",
" [0.03, 0],\n",
" [0.52, 0],\n",
" [0.38, 0], \n",
" [0.82, 1], \n",
" [0.33, 0], \n",
" [0.42, 0], \n",
" [0.55, 1], \n",
" [0.59, 0], \n",
" [0.09, 0], \n",
" [0.21, 0], \n",
" [0.43, 0], \n",
" [0.04, 0], \n",
" [0.08, 0], \n",
" [0.13, 0], \n",
" [0.01, 0], \n",
" [0.79, 1], \n",
" [0.42, 0], \n",
" [0.29, 0], \n",
" [0.08, 0],\n",
" [0.02, 0]\n",
" ]\n",
"\n",
"table = pd.DataFrame(data, columns = ['Propensity of 1', 'Actual'])\n",
"print(table)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# b.\n",
"Create a decile lift chart."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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