From 734e3c832bc7506e5b3f9d178de0a3e9f5892b99 Mon Sep 17 00:00:00 2001 From: Lan Zagar Date: Fri, 15 May 2015 09:47:06 +0200 Subject: [PATCH] Update learners.ipynb --- tutorials/learners.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/learners.ipynb b/tutorials/learners.ipynb index a723f72ae5a..cd408105efe 100644 --- a/tutorials/learners.ipynb +++ b/tutorials/learners.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Learners\n", + "# Custom learners in Orange\n", "Orange contains many learners which can be used to fit models for classification, regression and other tasks. But it is also very simple to write your own learner. To start, define a subclass of the `Orange.classification.Learner` base class and implement either one or both of the fit methods: `fit` works on data matrices represented as numpy arrays, while the more general `fit_storage` uses the encapsulating `Orange.data.Storage` object (or a subclass such as `Orange.data.Table`).\n", "After the necessary computations, the learner should produce a fitted model object, derived from the `Orange.classification.Model` base class, which needs to implement `predict` or `predict_storage`." ]