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franzi
2023-04-28 20:51:22 +02:00
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notebooks/5a_quality_prediction.ipynb
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@@ -156,7 +156,7 @@
"\n",
"Get creative :-)\n",
"\n",
"**Tip:** To use your model within the app's backend later (notebook `b`), it's important that your final model incl. all necessary preprocessing steps are combined in a single estimator object . This can be accomplished with sklearn's [`make_pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html) function. If necessary, you could even write a [custom transformer](https://towardsdatascience.com/pipelines-custom-transformers-in-scikit-learn-the-step-by-step-guide-with-python-code-4a7d9b068156) to perform more fancy feature engineering steps than what is provided by sklearn.\n"
"**Tip:** To use your model within the app's backend later (notebook `b`), it's important that your final model incl. all necessary preprocessing steps are combined in a single estimator object. This can be accomplished with sklearn's [`make_pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html) function. If necessary, you could even write a [custom transformer](https://towardsdatascience.com/pipelines-custom-transformers-in-scikit-learn-the-step-by-step-guide-with-python-code-4a7d9b068156) to perform more fancy feature engineering steps than what is provided by sklearn.\n"
]
},
{
@@ -177,7 +177,7 @@
"outputs": [],
"source": [
"# for the prediction task we use only the 28-day samples \n",
"df = df.loc[df[\"age\"] == 28].reset_index()\n",
"df = df.loc[df[\"age\"] == 28].reset_index(drop=True)\n",
"df"
]
},