From b0ee6d3e90000e1a9e4155ed1a35db5c3b0e09f9 Mon Sep 17 00:00:00 2001 From: deepyaman Date: Mon, 16 Sep 2024 17:06:15 +0000 Subject: [PATCH] deploy: a41046420108b988985b69a67b92c5eefee7ca6b --- index.html | 6 +-- reference/support-matrix/index.html | 6 +-- search.json | 18 ++++---- tutorial/pytorch.html | 72 ++++++++++++++--------------- tutorial/scikit-learn.html | 38 +++++++-------- tutorial/xgboost.html | 52 ++++++++++----------- 6 files changed, 96 insertions(+), 96 deletions(-) diff --git a/index.html b/index.html index 87ec510..1a28d9b 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
 
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index d418be0..e19a134 100644
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