diff --git a/20240513-uba-calibracion/ejemplo.ipynb b/20240513-uba-calibracion/ejemplo.ipynb index 33f8f3e..721635b 100644 --- a/20240513-uba-calibracion/ejemplo.ipynb +++ b/20240513-uba-calibracion/ejemplo.ipynb @@ -9,18 +9,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -28,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -120,7 +111,7 @@ "" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -153,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -174,57 +165,57 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
\n", + "
\n", "\n", "\n", @@ -337,10 +328,10 @@ "2 2333 24 furniture/equipment \n", "3 2039 18 furniture/equipment \n", "4 2728 15 radio/TV \n", - "5 1444 15 radio/TV , _body=, _boxhead=Boxhead([ColInfo(var='Risk', type=, column_label='Risk', column_align='right', column_width=None), ColInfo(var='Age', type=, column_label='Age', column_align='right', column_width=None), ColInfo(var='Sex', type=, column_label='Sex', column_align='left', column_width=None), ColInfo(var='Job', type=, column_label='Job', column_align='right', column_width=None), ColInfo(var='Housing', type=, column_label='Housing', column_align='left', column_width=None), ColInfo(var='Saving accounts', type=, column_label='Saving accounts', column_align='left', column_width=None), ColInfo(var='Checking account', type=, column_label='Checking account', column_align='left', column_width=None), ColInfo(var='Credit amount', type=, column_label='Credit amount', column_align='right', column_width=None), ColInfo(var='Duration', type=, 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category='source_notes', type='value', value='#D3D3D3'), source_notes_border_lr_style=OptionsInfo(scss=True, category='source_notes', type='value', value='none'), source_notes_border_lr_width=OptionsInfo(scss=True, category='source_notes', type='px', value='2px'), source_notes_border_lr_color=OptionsInfo(scss=True, category='source_notes', type='value', value='#D3D3D3'), source_notes_multiline=OptionsInfo(scss=False, category='source_notes', type='boolean', value=True), source_notes_sep=OptionsInfo(scss=False, category='source_notes', type='value', value=' '), container_width=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_height=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_padding_x=OptionsInfo(scss=False, category='container', type='px', value='0px'), container_padding_y=OptionsInfo(scss=False, category='container', type='px', value='10px'), container_overflow_x=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), container_overflow_y=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), quarto_disable_processing=OptionsInfo(scss=False, category='quarto', type='logical', value=False), quarto_use_bootstrap=OptionsInfo(scss=False, category='quarto', type='logical', value=False)), _has_built=False)" + "5 1444 15 radio/TV , _body=, _boxhead=Boxhead([ColInfo(var='Risk', type=, column_label='Risk', column_align='right', column_width=None), ColInfo(var='Age', type=, column_label='Age', column_align='right', column_width=None), ColInfo(var='Sex', type=, column_label='Sex', column_align='left', column_width=None), ColInfo(var='Job', type=, column_label='Job', column_align='right', column_width=None), ColInfo(var='Housing', type=, column_label='Housing', column_align='left', column_width=None), ColInfo(var='Saving accounts', type=, column_label='Saving accounts', column_align='left', column_width=None), ColInfo(var='Checking account', type=, column_label='Checking account', column_align='left', column_width=None), ColInfo(var='Credit amount', type=, column_label='Credit amount', column_align='right', column_width=None), ColInfo(var='Duration', type=, column_label='Duration', column_align='right', column_width=None), ColInfo(var='Purpose', type=, column_label='Purpose', column_align='left', column_width=None)]), _stub=Stub([RowInfo(rownum_i=0, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=1, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=2, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=3, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=4, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=5, group_id=None, rowname=None, group_label=None, built=False)]), _row_groups=[], _group_rows=GroupRows([]), _spanners=Spanners([]), _heading=Heading(title=None, subtitle=None, preheader=None), _stubhead=None, _source_notes=[], _footnotes=[], _styles=[StyleInfo(locname='data', locnum=5, grpname=None, colname='Age', rownum=0, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Age', rownum=1, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Age', rownum=2, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Age', rownum=3, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Age', rownum=4, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Age', rownum=5, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Sex', rownum=0, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Sex', rownum=1, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Sex', rownum=2, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Sex', rownum=3, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Sex', rownum=4, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Sex', rownum=5, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Job', rownum=0, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Job', rownum=1, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Job', rownum=2, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Job', rownum=3, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Job', rownum=4, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Job', rownum=5, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Housing', rownum=0, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Housing', rownum=1, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Housing', rownum=2, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Housing', rownum=3, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Housing', rownum=4, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Housing', rownum=5, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Saving accounts', rownum=0, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Saving accounts', rownum=1, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Saving accounts', rownum=2, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Saving accounts', rownum=3, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Saving accounts', rownum=4, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Saving 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rownum=0, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Credit amount', rownum=1, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Credit amount', rownum=2, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Credit amount', rownum=3, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Credit amount', rownum=4, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Credit amount', rownum=5, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Duration', rownum=0, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Duration', rownum=1, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Duration', rownum=2, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Duration', rownum=3, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Duration', rownum=4, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Duration', rownum=5, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Purpose', rownum=0, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Purpose', rownum=1, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Purpose', rownum=2, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Purpose', rownum=3, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Purpose', rownum=4, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Purpose', rownum=5, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=0, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=1, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=2, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=3, colnum=None, styles=[CellStyleFill(color='#BDCBCC')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=4, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=5, colnum=None, styles=[CellStyleFill(color='white')]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=0, colnum=None, styles=[CellStyleText(color='red', font=None, size=None, align=None, v_align=None, style=None, weight='bold', stretch=None, decorate=None, transform=None, whitespace=None)]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=1, colnum=None, styles=[CellStyleText(color='red', font=None, size=None, align=None, v_align=None, style=None, weight='bold', stretch=None, decorate=None, transform=None, whitespace=None)]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=2, colnum=None, styles=[CellStyleText(color='red', font=None, size=None, align=None, v_align=None, style=None, weight='bold', stretch=None, decorate=None, transform=None, whitespace=None)]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=3, colnum=None, styles=[CellStyleText(color='red', font=None, size=None, align=None, v_align=None, style=None, weight='bold', stretch=None, decorate=None, transform=None, whitespace=None)]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=4, colnum=None, styles=[CellStyleText(color='red', font=None, size=None, align=None, v_align=None, style=None, weight='bold', stretch=None, decorate=None, transform=None, whitespace=None)]), StyleInfo(locname='data', locnum=5, grpname=None, colname='Risk', rownum=5, colnum=None, styles=[CellStyleText(color='red', font=None, size=None, align=None, v_align=None, style=None, weight='bold', stretch=None, decorate=None, transform=None, whitespace=None)])], _locale=, _formats=[], _substitutions=[], _options=Options(table_id=OptionsInfo(scss=False, category='table', type='value', value=None), table_caption=OptionsInfo(scss=False, category='table', type='value', value=None), 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table_border_top_include=OptionsInfo(scss=False, category='table', type='boolean', value=True), table_border_top_style=OptionsInfo(scss=True, category='table', type='value', value='solid'), table_border_top_width=OptionsInfo(scss=True, category='table', type='px', value='2px'), table_border_top_color=OptionsInfo(scss=True, category='table', type='value', value='#A8A8A8'), table_border_right_style=OptionsInfo(scss=True, category='table', type='value', value='none'), table_border_right_width=OptionsInfo(scss=True, category='table', type='px', value='2px'), table_border_right_color=OptionsInfo(scss=True, category='table', type='value', value='#D3D3D3'), table_border_bottom_include=OptionsInfo(scss=False, category='table', type='boolean', value=True), table_border_bottom_style=OptionsInfo(scss=True, category='table', type='value', value='solid'), table_border_bottom_width=OptionsInfo(scss=True, category='table', type='px', value='2px'), table_border_bottom_color=OptionsInfo(scss=True, category='table', type='value', value='#A8A8A8'), table_border_left_style=OptionsInfo(scss=True, category='table', type='value', value='none'), table_border_left_width=OptionsInfo(scss=True, category='table', type='px', value='2px'), table_border_left_color=OptionsInfo(scss=True, category='table', type='value', value='#D3D3D3'), heading_background_color=OptionsInfo(scss=True, category='heading', type='value', value=None), heading_align=OptionsInfo(scss=True, category='heading', type='value', value='center'), heading_title_font_size=OptionsInfo(scss=True, category='heading', type='px', value='125%'), heading_title_font_weight=OptionsInfo(scss=True, category='heading', type='value', value='initial'), heading_subtitle_font_size=OptionsInfo(scss=True, category='heading', type='px', value='85%'), heading_subtitle_font_weight=OptionsInfo(scss=True, category='heading', type='value', value='initial'), heading_padding=OptionsInfo(scss=True, category='heading', type='px', value='4px'), 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category='row_group', type='value', value='initial'), row_group_text_transform=OptionsInfo(scss=True, category='row_group', type='value', value='inherit'), row_group_padding=OptionsInfo(scss=True, category='row_group', type='px', value='8px'), row_group_padding_horizontal=OptionsInfo(scss=True, category='row_group', type='px', value='5px'), row_group_border_top_style=OptionsInfo(scss=True, category='row_group', type='value', value='solid'), row_group_border_top_width=OptionsInfo(scss=True, category='row_group', type='px', value='2px'), row_group_border_top_color=OptionsInfo(scss=True, category='row_group', type='value', value='#D3D3D3'), row_group_border_right_style=OptionsInfo(scss=True, category='row_group', type='value', value='none'), row_group_border_right_width=OptionsInfo(scss=True, category='row_group', type='px', value='1px'), row_group_border_right_color=OptionsInfo(scss=True, category='row_group', type='value', value='#D3D3D3'), 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Estadística descriptiva - Variables numéricas
 countmeanstdmin25%50%75%maxcountmeanstdmin25%50%75%max
Age1000.0035.5511.3819.0027.0033.0042.0075.00Age1000.0035.5511.3819.0027.0033.0042.0075.00
Job1000.001.900.650.002.002.002.003.00Job1000.001.900.650.002.002.002.003.00
Credit amount1000.003271.262822.74250.001365.502319.503972.2518424.00Credit amount1000.003271.262822.74250.001365.502319.503972.2518424.00
Duration1000.0020.9012.064.0012.0018.0024.0072.00Duration1000.0020.9012.064.0012.0018.0024.0072.00
Risk1000.000.300.460.000.000.001.001.00Risk1000.000.300.460.000.000.001.001.00
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -517,101 +508,101 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
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Estadística descriptiva - Variables categóricas
 countuniquetopfreqcountuniquetopfreq
Sex10002male690Sex10002male690
Housing10003own713Housing10003own713
Saving accounts8174little603Saving accounts8174little603
Checking account6063little274Checking account6063little274
Purpose10008car337Purpose10008car337
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -634,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -701,7 +692,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -750,19 +741,18 @@ " \n", " \n", " \n", - " 949\n", - " 0.21\n", - " 0.67\n", - " 0.24\n", + " 975\n", + " 0.68\n", + " 0.33\n", + " 0.07\n", " 0.36\n", - " 1.0\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", - " ...\n", " 0.0\n", + " ...\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -770,30 +760,31 @@ " 0.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " \n", " \n", - " 54\n", - " 0.68\n", - " 0.67\n", - " 0.14\n", - " 0.57\n", - " 1.0\n", + " 712\n", + " 0.48\n", + " 1.00\n", + " 0.15\n", + " 0.30\n", " 1.0\n", " 0.0\n", - " 0.0\n", " 1.0\n", " 0.0\n", - " ...\n", " 0.0\n", " 0.0\n", + " ...\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 1.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " \n", @@ -804,33 +795,33 @@ ], "text/plain": [ " Age Job Credit amount Duration Sex_male Housing_free Housing_own \\\n", - "949 0.21 0.67 0.24 0.36 1.0 0.0 1.0 \n", - "54 0.68 0.67 0.14 0.57 1.0 1.0 0.0 \n", + "975 0.68 0.33 0.07 0.36 0.0 0.0 1.0 \n", + "712 0.48 1.00 0.15 0.30 1.0 0.0 1.0 \n", "\n", " Housing_rent Saving accounts_little Saving accounts_moderate ... \\\n", - "949 0.0 0.0 1.0 ... \n", - "54 0.0 1.0 0.0 ... \n", + "975 0.0 0.0 0.0 ... \n", + "712 0.0 0.0 0.0 ... \n", "\n", " Saving accounts_nan Checking account_little Checking account_moderate \\\n", - "949 0.0 0.0 0.0 \n", - "54 0.0 0.0 1.0 \n", + "975 0.0 0.0 0.0 \n", + "712 1.0 0.0 0.0 \n", "\n", " Checking account_rich Checking account_nan Purpose_business \\\n", - "949 0.0 1.0 0.0 \n", - "54 0.0 0.0 0.0 \n", + "975 1.0 0.0 0.0 \n", + "712 0.0 1.0 0.0 \n", "\n", " Purpose_car Purpose_furniture/equipment Purpose_radio/TV \\\n", - "949 0.0 0.0 1.0 \n", - "54 1.0 0.0 0.0 \n", + "975 0.0 0.0 1.0 \n", + "712 1.0 0.0 0.0 \n", "\n", " Purpose_infrequent_sklearn \n", - "949 0.0 \n", - "54 0.0 \n", + "975 0.0 \n", + "712 0.0 \n", "\n", "[2 rows x 22 columns]" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -869,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -881,14 +872,14 @@ " SimpleImputer(strategy='median')),\n", " ('scaler',\n", " MinMaxScaler())]),\n", - " <sklearn.compose._column_transformer.make_column_selector object at 0x16a23b910>),\n", + " <sklearn.compose._column_transformer.make_column_selector object at 0x1589814e0>),\n", " ('cat',\n", " Pipeline(steps=[('ohe',\n", " OneHotEncoder(drop='if_binary',\n", " handle_unknown='infrequent_if_exist',\n", " min_frequency=0.05,\n", " sparse_output=False))]),\n", - " <sklearn.compose._column_transformer.make_column_selector object at 0x16a238c40>)],\n", + " <sklearn.compose._column_transformer.make_column_selector object at 0x158983f40>)],\n", " verbose_feature_names_out=False)),\n", " ('modelo',\n", " HistGradientBoostingClassifier(max_depth=4, max_iter=1000,\n", @@ -898,14 +889,14 @@ " SimpleImputer(strategy='median')),\n", " ('scaler',\n", " MinMaxScaler())]),\n", - " <sklearn.compose._column_transformer.make_column_selector object at 0x16a23b910>),\n", + " <sklearn.compose._column_transformer.make_column_selector object at 0x1589814e0>),\n", " ('cat',\n", " Pipeline(steps=[('ohe',\n", " OneHotEncoder(drop='if_binary',\n", " handle_unknown='infrequent_if_exist',\n", " min_frequency=0.05,\n", " sparse_output=False))]),\n", - " <sklearn.compose._column_transformer.make_column_selector object at 0x16a238c40>)],\n", + " <sklearn.compose._column_transformer.make_column_selector object at 0x158983f40>)],\n", " verbose_feature_names_out=False)),\n", " ('modelo',\n", " HistGradientBoostingClassifier(max_depth=4, max_iter=1000,\n", @@ -913,15 +904,15 @@ " Pipeline(steps=[('impute',\n", " SimpleImputer(strategy='median')),\n", " ('scaler', MinMaxScaler())]),\n", - " <sklearn.compose._column_transformer.make_column_selector object at 0x16a23b910>),\n", + " <sklearn.compose._column_transformer.make_column_selector object at 0x1589814e0>),\n", " ('cat',\n", " Pipeline(steps=[('ohe',\n", " OneHotEncoder(drop='if_binary',\n", " handle_unknown='infrequent_if_exist',\n", " min_frequency=0.05,\n", " sparse_output=False))]),\n", - " <sklearn.compose._column_transformer.make_column_selector object at 0x16a238c40>)],\n", - " verbose_feature_names_out=False)
<sklearn.compose._column_transformer.make_column_selector object at 0x16a23b910>
SimpleImputer(strategy='median')
MinMaxScaler()
<sklearn.compose._column_transformer.make_column_selector object at 0x16a238c40>
OneHotEncoder(drop='if_binary', handle_unknown='infrequent_if_exist',\n",
+       "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x158983f40>)],\n",
+       "                  verbose_feature_names_out=False)
<sklearn.compose._column_transformer.make_column_selector object at 0x1589814e0>
SimpleImputer(strategy='median')
MinMaxScaler()
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OneHotEncoder(drop='if_binary', handle_unknown='infrequent_if_exist',\n",
        "              min_frequency=0.05, sparse_output=False)
HistGradientBoostingClassifier(max_depth=4, max_iter=1000, random_state=42)
" ], "text/plain": [ @@ -931,21 +922,21 @@ " SimpleImputer(strategy='median')),\n", " ('scaler',\n", " MinMaxScaler())]),\n", - " ),\n", + " ),\n", " ('cat',\n", " Pipeline(steps=[('ohe',\n", " OneHotEncoder(drop='if_binary',\n", " handle_unknown='infrequent_if_exist',\n", " min_frequency=0.05,\n", " sparse_output=False))]),\n", - " )],\n", + " )],\n", " verbose_feature_names_out=False)),\n", " ('modelo',\n", " HistGradientBoostingClassifier(max_depth=4, max_iter=1000,\n", " random_state=42))])" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -968,7 +959,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -976,38 +967,38 @@ "text/html": [ "\n", - "\n", + "
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 AccuracyPrecisionRecallF1ROC AUCLog LossBrier LossAccuracyPrecisionRecallF1ROC AUCLog LossBrier Loss
Hist gradient boosting0.720.530.530.530.740.930.22Hist gradient boosting0.720.530.530.530.740.930.22
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1034,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1097,7 +1088,7 @@ "443 1 1 0.6428 (0.23, 0.84]" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1110,7 +1101,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1153,28 +1144,28 @@ " (0.0, 0.02]\n", " 50\n", " 0.14\n", - " 0.00\n", + " 0.01\n", " \n", " \n", " 2\n", " (0.02, 0.23]\n", " 50\n", " 0.28\n", - " 0.00\n", + " 0.08\n", " \n", " \n", " 3\n", " (0.23, 0.84]\n", " 50\n", " 0.40\n", - " 0.52\n", + " 0.51\n", " \n", " \n", " 4\n", " (0.84, 1.0]\n", " 50\n", " 0.60\n", - " 1.00\n", + " 0.95\n", " \n", " \n", "\n", @@ -1183,13 +1174,13 @@ "text/plain": [ " bin N frac_positive avg_pred\n", "0 (-0.0, 0.0] 50 0.08 0.00\n", - "1 (0.0, 0.02] 50 0.14 0.00\n", - "2 (0.02, 0.23] 50 0.28 0.00\n", - "3 (0.23, 0.84] 50 0.40 0.52\n", - "4 (0.84, 1.0] 50 0.60 1.00" + "1 (0.0, 0.02] 50 0.14 0.01\n", + "2 (0.02, 0.23] 50 0.28 0.08\n", + "3 (0.23, 0.84] 50 0.40 0.51\n", + "4 (0.84, 1.0] 50 0.60 0.95" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1199,14 +1190,14 @@ " .agg(\n", " N = ('y_true','count'), \n", " frac_positive=('y_true','mean'),\n", - " avg_pred = ('y_pred','mean')\n", + " avg_pred = ('y_pred_prob','mean')\n", " ).round(2).reset_index()\n", ")" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1248,7 +1239,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1276,30 +1267,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 16, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/quarto-env/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/opt/anaconda3/envs/quarto-env/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_calibration(\n", " preds=preds,\n", @@ -1310,7 +1280,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1344,7 +1314,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1639,7 +1609,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ {