
## Baseline Model trained on tips to predict sex

Metrics of the best model:

accuracy             0.647364

average_precision    0.481257

roc_auc              0.608805

recall_macro         0.588751

f1_macro             0.588435

Name: MultinomialNB(), dtype: float64



See model plot below:

<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])),(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])),(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline: Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">MinMaxScaler</label><div class="sk-toggleable__content"><pre>MinMaxScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div>
{"license": "apache-2.0", "library_name": "sklearn"}
sklearn license:apache-2.0 region:us
TAGS
#sklearn #license-apache-2.0 #region-us 


## Baseline Model trained on tips to predict sex

Metrics of the best model:

accuracy             0.647364

average_precision    0.481257

roc_auc              0.608805

recall_macro         0.588751

f1_macro             0.588435

Name: MultinomialNB(), dtype: float64



See model plot below:

<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 URL-toggleable {background-color: white;}#sk-container-id-2 URL-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 URL-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 URL-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 URL-estimator:hover URL-toggleable__label-arrow:before {color: black;}#sk-container-id-2 URL-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 URL-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 URL-toggleable__control:checked~URL-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 URL-toggleable__control:checked~URL-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 URL-estimator URL-toggleable__control:checked~URL-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 URL-label URL-toggleable__control:checked~URL-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 URL-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 URL-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 URL-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 URL-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 URL-label:hover URL-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 URL-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 URL-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 URL-item {position: relative;z-index: 1;}#sk-container-id-2 URL-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 URL-item::before, #sk-container-id-2 URL-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 URL-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 URL-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 URL-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 URL-parallel-item:only-child::after {width: 0;}#sk-container-id-2 URL-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 URL-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 URL-label-container {text-align: center;}#sk-container-id-2 URL-container {/* jupyter's 'URL' sets '[hidden] { display: none; }' but URL set '[hidden] { display: none !important; }' so we also need the '!important' here to be able to override the default hidden behavior on the sphinx rendered URL. See: URL */display: inline-block !important;position: relative;}#sk-container-id-2 URL-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])),(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with URL.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])),(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline: Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">MinMaxScaler</label><div class="sk-toggleable__content"><pre>MinMaxScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div>
## Baseline Model trained on tips to predict sex

Metrics of the best model:

accuracy             0.647364

average_precision    0.481257

roc_auc              0.608805

recall_macro         0.588751

f1_macro             0.588435

Name: MultinomialNB(), dtype: float64



See model plot below:

<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 URL-toggleable {background-color: white;}#sk-container-id-2 URL-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 URL-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 URL-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 URL-estimator:hover URL-toggleable__label-arrow:before {color: black;}#sk-container-id-2 URL-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 URL-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 URL-toggleable__control:checked~URL-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 URL-toggleable__control:checked~URL-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 URL-estimator URL-toggleable__control:checked~URL-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 URL-label URL-toggleable__control:checked~URL-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 URL-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 URL-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 URL-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 URL-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 URL-label:hover URL-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 URL-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 URL-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 URL-item {position: relative;z-index: 1;}#sk-container-id-2 URL-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 URL-item::before, #sk-container-id-2 URL-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 URL-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 URL-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 URL-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 URL-parallel-item:only-child::after {width: 0;}#sk-container-id-2 URL-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 URL-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 URL-label-container {text-align: center;}#sk-container-id-2 URL-container {/* jupyter's 'URL' sets '[hidden] { display: none; }' but URL set '[hidden] { display: none !important; }' so we also need the '!important' here to be able to override the default hidden behavior on the sphinx rendered URL. See: URL */display: inline-block !important;position: relative;}#sk-container-id-2 URL-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])),(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with URL.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])),(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline: Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">MinMaxScaler</label><div class="sk-toggleable__content"><pre>MinMaxScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div>
TAGS
#sklearn #license-apache-2.0 #region-us 
 ## Baseline Model trained on tips to predict sex

Metrics of the best model:

accuracy             0.647364

average_precision    0.481257

roc_auc              0.608805

recall_macro         0.588751

f1_macro             0.588435

Name: MultinomialNB(), dtype: float64



See model plot below:

<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 URL-toggleable {background-color: white;}#sk-container-id-2 URL-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 URL-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 URL-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 URL-estimator:hover URL-toggleable__label-arrow:before {color: black;}#sk-container-id-2 URL-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 URL-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 URL-toggleable__control:checked~URL-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 URL-toggleable__control:checked~URL-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 URL-estimator URL-toggleable__control:checked~URL-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 URL-label URL-toggleable__control:checked~URL-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 URL-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 URL-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 URL-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 URL-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 URL-label:hover URL-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 URL-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 URL-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 URL-item {position: relative;z-index: 1;}#sk-container-id-2 URL-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 URL-item::before, #sk-container-id-2 URL-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 URL-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 URL-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 URL-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 URL-parallel-item:only-child::after {width: 0;}#sk-container-id-2 URL-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 URL-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 URL-label-container {text-align: center;}#sk-container-id-2 URL-container {/* jupyter's 'URL' sets '[hidden] { display: none; }' but URL set '[hidden] { display: none !important; }' so we also need the '!important' here to be able to override the default hidden behavior on the sphinx rendered URL. See: URL */display: inline-block !important;position: relative;}#sk-container-id-2 URL-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])),(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with URL.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])),(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types=            continuous  dirty_float  low_card_int  ...   date  free_string  useless
total_bill        True        False         False  ...  False        False    False
tip               True        False         False  ...  False        False    False
smoker           False        False         False  ...  False        False    False
day              False        False         False  ...  False        False    False
time             False        False         False  ...  False        False    False
size             False        False          True  ...  False        False    False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline: Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),(&#x27;multinomialnb&#x27;, MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">MinMaxScaler</label><div class="sk-toggleable__content"><pre>MinMaxScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div>
passage: TAGS
#sklearn #license-apache-2.0 #region-us 
