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(This article was first published on <strong><a href="https://r-posts.com/does-imputing-model-labels-using-the-model-predictions-can-improve-its-performance/"> R-posts.com</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>In some scenarios a data scientist may want to train a model for which there exists an abundance of observations, but only a small fraction of is labeled, making the sample size available to train the model rather small. Although there’s plenty of literature on the subject (e.g. “Active learning”, “Semi-supervised learning” etc) one may be tempted (maybe due to fast approaching deadlines) to train a model with the labelled data and use it to impute the missing labels.</p>
<p>While for some the above suggestion might seem simply incorrect, I have encountered such suggestions on several occasions and had a hard time refuting them. To make sure it wasn’t just the type of places I work at I went and asked around in 2 Israeli (sorry non Hebrew readers) machine learning oriented Facebook groups about their opinion:<span> </span><a rel="nofollow" href="https://www.facebook.com/groups/543283492502370/permalink/1158551544308892/" target="_blank">Machine &amp; Deep learning Israel</a><span> </span>and<span> </span><a rel="nofollow" href="https://www.facebook.com/groups/statprob/permalink/767687730239611/" target="_blank">Statistics and probability group</a>. While many were referring me to methods discussed in the literature, almost no one indicated the proposed method was utterly wrong.</p>
<p>I decided to perform a simulation study to get a definitive answer once and for all. If you’re interested in reading what were the results see <a rel="nofollow" href="https://github.com/IyarLin/imputing_labels_with_model_predictions/blob/master/imputing_labels_with_model_predictions.md" target="_blank">my analysis on Github</a>.</p>
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