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drmingle/Make Simulated Classification Data.ipynb

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Make Simulated Classification Data.ipynb
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"source": [
"---\n",
"title: \"Make Simulated Data For Classification\"\n",
"author: \"Damian Mingle\"\n",
"date: 04/30/2018\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preliminaries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"from sklearn.datasets import make_classification\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Simulated Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Create a simulated feature matrix with output vector using 1000 samples,\n",
"features, output = make_classification(n_samples = 1000,\n",
" # eight features\n",
" n_features = 8,\n",
" # five features that actually predict the output's classes\n",
" n_informative = 5,\n",
" # three features that are are random to class output\n",
" n_redundant = 3,\n",
" # four classes output\n",
" n_classes = 4,\n",
" # with 15% of observations in the 1st class, 5% in the 2nd class, \n",
" # 30% in the 3rd class, and 50% in the third class. \n",
" weights = [.15,.05, .3, .5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## View Data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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"source": [
"# View the first five observations and their 10 features\n",
"pd.DataFrame(features).head()"
]
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"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
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"source": [
"# View the first five observation's classes\n",
"pd.DataFrame(output).head()"
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