Fiona Gregory<p>This series of videos on machine learning algorithms (Lab a through Lab d, so far) by Courage Kamusoku are the best explanations I've seen yet. How the models actually work, their strengths and weaknesses, and what you are actually solving for when you tune the hyperparameters. <a href="https://www.youtube.com/@couragekamusoko5689/videos" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">youtube.com/@couragekamusoko56</span><span class="invisible">89/videos</span></a><br><a href="https://mapstodon.space/tags/SVM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SVM</span></a> <a href="https://mapstodon.space/tags/KNN" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>KNN</span></a> <a href="https://mapstodon.space/tags/DecisionTree" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DecisionTree</span></a> <a href="https://mapstodon.space/tags/RandomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForest</span></a></p>
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#decisiontree
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Joxean Koret (@matalaz)<p>Uhm... if I get a decision tree like the one shown in the picture, does it mean that I only need the columns shown in the tree for training and validation, right? I would only need the columns 2 and 3 (x[2], x[3]), isn't it? Or am I missing something else?</p><p><a href="https://mastodon.social/tags/Sklearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Sklearn</span></a> <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://mastodon.social/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://mastodon.social/tags/DecisionTree" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DecisionTree</span></a></p>
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