title: Quantile prediction for mlr3 graph learner
body: <p>I have a stacked learner where the output layer is a <code>regr.ranger</code> with params <code>list(rf.quantreg = TRUE, rf.keep.inbag = TRUE)</code>. Is it possible to predict quantiles with <code>GraphLearner</code>s like this?</p>
  <p>I know that for a pure <code>lrn('regr.ranger')</code>, once trained, I can simply reference the <code>ranger</code> model directly and use that for quantile prediction:</p>
  <pre class="lang-r prettyprint-override"><code>predict(my_learner$model, data = my_test_data, type = &quot;quantiles&quot;, quantiles = c(0.025, 0.975))
  </code></pre>
  <p>But for the stacked learner, I have other learners mediating between the features and <code>regr.ranger</code>, so it seems to me that I <em>have</em> to go via <code>mlr3</code>.</p>
  <p>My <code>GraphLearner</code> consists of some feature coding, a <code>regr.kknn</code> learner and a <code>regr.glm</code> learner plus some extras. Then <code>rf</code> (a <code>regr.ranger</code>) sits at the output level:</p>
  <p><a href="https://i.stack.imgur.com/V7OQy.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/V7OQy.png" alt="enter image description here" /></a></p>