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:
Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string uselessIn a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.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])),('pipeline',Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())]))])
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('easypreprocessor',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])),('pipeline',Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())]))])
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])
Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())])
MinMaxScaler()
MultinomialNB()
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