From dabc905a87c351f129276ed1f941c40f9f33ea28 Mon Sep 17 00:00:00 2001 From: deepyaman Date: Mon, 16 Sep 2024 17:30:41 +0000 Subject: [PATCH] deploy: 933f74897cd08135be0316b2759da9689fbe9dea --- index.html | 6 +- reference/support-matrix/index.html | 6 +- search.json | 18 +++--- tutorial/pytorch.html | 92 ++++++++++++++--------------- tutorial/scikit-learn.html | 78 ++++++++++++------------ tutorial/xgboost.html | 76 ++++++++++++------------ 6 files changed, 138 insertions(+), 138 deletions(-) diff --git a/index.html b/index.html index 1a28d9b..8a9ec34 100644 --- a/index.html +++ b/index.html @@ -258,7 +258,7 @@

Install IbisML

Create your first recipe

With recipes, you can define sequences of feature engineering steps to get your data ready for modeling. For example, create a recipe to replace missing values using the mean of each numeric column and then normalize numeric data to have a standard deviation of one and a mean of zero.

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import ibis_ml as ml
 
 imputer = ml.ImputeMean(ml.numeric())
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Create your first
 rec = ml.Recipe(imputer, scaler)

A recipe can be chained in a Pipeline like any other transformer.

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from sklearn.pipeline import Pipeline
 from sklearn.svm import SVC
 
 pipe = Pipeline([("rec", rec), ("svc", SVC())])

The pipeline can be used as any other estimator and avoids leaking the test set into the train set.

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from sklearn.datasets import make_classification
 from sklearn.model_selection import train_test_split
 
diff --git a/reference/support-matrix/index.html b/reference/support-matrix/index.html
index e19a134..cda6279 100644
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