<p dir="auto">I couldn't find any examples of this anywhere so here goes.</p>
<p dir="auto">What I have in mind is this:<br>
I have an already fit custom transformer and i want to use it in a pipeline with a model that is not fit to the transformed data yet.</p>
<p dir="auto">The goal is to keep the old fit transformer without refiting it to the new data and only fit the model, all combined in a Pipeline().<br>
Currently, if I try to do that I get and error that the model has to be fit first.</p>
<p dir="auto">The only alternative i can think of is transforming the data, training the model with it and then combining them into a Pipeline(). Or creating the Pipeline() and then fitting them step by step.</p>
<h3 dir="auto">Describe the workflow you want to enable</h3>
<p dir="auto">There is no cohesive way of knowing when a classifier will finish training. What is shown by <code class="notranslate">verbose = True</code> is not consistent across models.</p>
<h3 dir="auto">Describe your proposed solution</h3>
<p dir="auto">I propose wrapping all most/all <code class="notranslate">.fit()</code> functions in tqdm.</p>
<h3 dir="auto">Describe alternatives you've considered, if relevant</h3>
<p dir="auto"><em>No response</em></p>
<h3 dir="auto">Additional context</h3>
<p dir="auto"><em>No response</em></p>