--- license: apache-2.0 library_name: sklearn tags: - tabular-classification - baseline-trainer --- ## Baseline Model trained on heart1ohr2x9e to apply classification on target **Metrics of the best model:** accuracy 0.885854 average_precision 0.949471 roc_auc 0.050633 recall_macro 0.885324 f1_macro 0.885610 Name: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64 **See model plot below:**
Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types=          continuous  dirty_float  low_card_int  ...   date  free_string  useless
age            False        False         False  ...  False        False    False
sex            False        False         False  ...  False        False    False
cp             False        False         False  ...  False        False    False
trestbps        True        False         False  ...  False        False    False
chol            True        False         False  ...  False        False    False
fbs            False        False         False  ...  False        False    False
restecg        False        Fa......  False        False    False
thalach         True        False         False  ...  False        False    False
exang          False        False         False  ...  False        False    False
oldpeak         True        False         False  ...  False        False    False
slope          False        False         False  ...  False        False    False
ca             False        False         False  ...  False        False    False
thal           False        False         False  ...  False        False    False[13 rows x 7 columns])),('logisticregression',LogisticRegression(C=1, class_weight='balanced',max_iter=1000))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types=          continuous  dirty_float  low_card_int  ...   date  free_string  useless
age            False        False         False  ...  False        False    False
sex            False        False         False  ...  False        False    False
cp             False        False         False  ...  False        False    False
trestbps        True        False         False  ...  False        False    False
chol            True        False         False  ...  False        False    False
fbs            False        False         False  ...  False        False    False
restecg        False        Fa......  False        False    False
thalach         True        False         False  ...  False        False    False
exang          False        False         False  ...  False        False    False
oldpeak         True        False         False  ...  False        False    False
slope          False        False         False  ...  False        False    False
ca             False        False         False  ...  False        False    False
thal           False        False         False  ...  False        False    False[13 rows x 7 columns])),('logisticregression',LogisticRegression(C=1, class_weight='balanced',max_iter=1000))])EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless age False False False ... False False False sex False False False ... False False False cp False False False ... False False False trestbps True False False ... False False False chol True False False ... False False False fbs False False False ... False False False restecg False False False ... False False False thalach True False False ... False False False exang False False False ... False False False oldpeak True False False ... False False False slope False False False ... False False False ca False False False ... False False False thal False False False ... False False False[13 rows x 7 columns])
LogisticRegression(C=1, class_weight='balanced', max_iter=1000)