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"metadata": {
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},
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"outputs": [
{
"data": {
"text/html": [
"<style>\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
" @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Lato');\n",
" @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
"\n",
" \n",
" body {\n",
" font-family: 'Lora', Consolas, sans-serif;\n",
" \n",
" -webkit-print-color-adjust: exact important !;\n",
" \n",
" \n",
" \n",
" }\n",
" \n",
" .alert-block {\n",
" width: 95%;\n",
" margin: auto;\n",
" }\n",
" \n",
" .rendered_html code\n",
" {\n",
" color: black;\n",
" background: #eaf0ff;\n",
" background: #f5f5f5; \n",
" padding: 1pt;\n",
" font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
" }\n",
" \n",
" p {\n",
" line-height: 140%;\n",
" }\n",
" \n",
" strong code {\n",
" background: red;\n",
" }\n",
" \n",
" .rendered_html strong code\n",
" {\n",
" background: #f5f5f5;\n",
" }\n",
" \n",
" .CodeMirror pre {\n",
" font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
" }\n",
" \n",
" .cm-s-ipython span.cm-keyword {\n",
" font-weight: normal;\n",
" }\n",
" \n",
" strong {\n",
" background: #f5f5f5;\n",
" margin-top: 4pt;\n",
" margin-bottom: 4pt;\n",
" padding: 2pt;\n",
" border: 0.5px solid #a0a0a0;\n",
" font-weight: bold;\n",
" color: darkred;\n",
" }\n",
" \n",
" \n",
" div #notebook {\n",
" # font-size: 10pt; \n",
" line-height: 145%;\n",
" }\n",
" \n",
" li {\n",
" line-height: 145%;\n",
" }\n",
"\n",
" div.output_area pre {\n",
" background: #fff9d8 !important;\n",
" padding: 5pt;\n",
" \n",
" -webkit-print-color-adjust: exact; \n",
" \n",
" }\n",
" \n",
" \n",
" \n",
" h1, h2, h3, h4 {\n",
" font-family: Kameron, arial;\n",
"\n",
"\n",
" }\n",
" \n",
" div#maintoolbar {display: none !important;}\n",
"</style>\n",
" <script>\n",
"IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
" return false;\n",
"}\n",
" </script>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# IGNORE THIS CELL WHICH CUSTOMIZES LAYOUT AND STYLING OF THE NOTEBOOK !\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"import warnings\n",
"warnings.filterwarnings('ignore', category=FutureWarning)\n",
"from IPython.core.display import HTML; HTML(open(\"custom.html\", \"r\").read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chapter 2: Classification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we have learned in the previous chapter *classification* is a machine learning problem belonging to a group of *supervised learning* problems. In classification the aim is to learn how to predict a class of a categorical label, based on set of already labelled training examples (hence, supervised). Such labels (categories) and corresponding classes can be:\n",
"- sick: yes / no,\n",
"- character: good / bad / dont't know,\n",
"- digit: 0 / ... / 9.\n",
"In this chapter we introduce the core concepts of classification."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to build a simple classifier?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's quickly look again at the beer example:"
"execution_count": 2,
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>alcohol_content</th>\n",
" <th>bitterness</th>\n",
" <th>darkness</th>\n",
" <th>fruitiness</th>\n",
" <th>is_yummy</th>\n",
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