Baseline Model trained on tipsuhtxfu to apply classification on sex
Metrics of the best model:
accuracy 0.647364
average_precision 0.507660
roc_auc 0.625546
recall_macro 0.589832
f1_macro 0.585292
Name: MultinomialNB(), dtype: float64
See model plot below:
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 False ... False False False[6 rows x 7 columns])),('pipeline',Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())]))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 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 False ... 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 False ... False False False[6 rows x 7 columns])
Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())])MinMaxScaler()
MultinomialNB()
Disclaimer: This model is trained with dabl library as a baseline, for better results, use AutoTrain.
Logs of training including the models tried in the process can be found in logs.txt