Baseline Model trained on outhimar_64 to apply regression on Close
Metrics of the best model:
r2 0.999858
neg_mean_squared_error -1.067685
Name: Ridge(alpha=10), dtype: float64
See model plot below:
Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
Date False False False ... True False False
Open True False False ... False False False
High True False False ... False False False
Low True False False ... False False False
Adj Close True False False ... False False False
Volume True False False ... False False False[6 rows x 7 columns])),('ridge', Ridge(alpha=10))])
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Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
Date False False False ... True False False
Open True False False ... False False False
High True False False ... False False False
Low True False False ... False False False
Adj Close True False False ... False False False
Volume True False False ... False False False[6 rows x 7 columns])),('ridge', Ridge(alpha=10))])EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Date False False False ... True False False Open True False False ... False False False High True False False ... False False False Low True False False ... False False False Adj Close True False False ... False False False Volume True False False ... False False False[6 rows x 7 columns])
Ridge(alpha=10)
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