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MTN Hyperparameter tuning with grid search
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notebooks/parameter_tuning_grid_search.ipynb

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"explore for each parameter. Thus, adding new parameters with their associated\n",
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"values to be explored rapidly becomes computationally expensive. Because of\n",
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"that, here we only explore the combination learning-rate and the maximum\n",
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"number of nodes for a total of 4 x 3 = 12 combinations.\n",
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"\n",
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"number of nodes for a total of 4 x 3 = 12 combinations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"\n",

python_scripts/parameter_tuning_grid_search.py

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# that, here we only explore the combination learning-rate and the maximum
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# number of nodes for a total of 4 x 3 = 12 combinations.
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# %%
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# %%time
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from sklearn.model_selection import GridSearchCV
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