--- license: apache-2.0 library_name: sklearn tags: - tabular-classification - baseline-trainer --- ## Baseline Model trained on irisg444_4c0 to apply classification on Species **Metrics of the best model:** accuracy 0.953333 recall_macro 0.953333 precision_macro 0.956229 f1_macro 0.953216 Name: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64 **See model plot below:**
Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
SepalLengthCm True False ... False False
SepalWidthCm True False ... False False
PetalLengthCm True False ... False False
PetalWidthCm True False ... False False[4 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 ... free_string useless
SepalLengthCm True False ... False False
SepalWidthCm True False ... False False
PetalLengthCm True False ... False False
PetalWidthCm True False ... False False[4 rows x 7 columns])),('logisticregression',LogisticRegression(C=1, class_weight='balanced',max_iter=1000))])EasyPreprocessor(types= continuous dirty_float ... free_string useless SepalLengthCm True False ... False False SepalWidthCm True False ... False False PetalLengthCm True False ... False False PetalWidthCm True False ... False False[4 rows x 7 columns])
LogisticRegression(C=1, class_weight='balanced', max_iter=1000)