From 9b39c1b22c3402c7224cabc58a2a2601be82d223 Mon Sep 17 00:00:00 2001 From: franzi Date: Fri, 28 Apr 2023 20:51:22 +0200 Subject: [PATCH] minor --- notebooks/5a_quality_prediction.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/5a_quality_prediction.ipynb b/notebooks/5a_quality_prediction.ipynb index 401bffc..6e843e2 100644 --- a/notebooks/5a_quality_prediction.ipynb +++ b/notebooks/5a_quality_prediction.ipynb @@ -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" ] },