<p>With deep-learning models you typically have enough degrees of freedom for the model to learn structure in the feature space that's useful for predictions. If there are two groups with different characteristics (e.g. apes and humans), and knowing this group is useful in making a prediction, the model should be able to learn this.</p>

<p>Additionally if your ultimate aim is to classify, it's common in deep-learning models to have a <a href="https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=softmax#mxnet.ndarray.softmax" rel="nofollow noreferrer">softmax layer</a> as an output which can be interpreted as a probability of given class; the higher this probability is the more confident you can be in the prediction. You should calibrate and evaluate this probability as suggested in <a href="https://arxiv.org/pdf/1706.04599.pdf" rel="nofollow noreferrer">this paper</a>.</p>

<p>On the other hand, if you're looking to apply more simple models (e.g. linear models), you may want to perform unsupervised learning beforehand and include this as a categorical feature in your model. As suggested by  Viacheslav a clustering algorithm like K-Means could work for your dataset, otherwise you may want to look at Gaussian Mixture Models or DBSCAN.</p>
Best way to train only predictable samples in the dataset for R MXNet
<p>I have a training set looks something like this.</p>

<p>features:categorical/numerical</p>

<p>output:binary 1/0</p>

<pre><code>[1] feature[1][1] feature[1][2] ... feature[1][j]
[2] feature[2][1] feature[2][2] ... feature[2][j]
.
.
.
[i] feature[i][1] feature[i][2] ... feature[i][j]
</code></pre>

<p>Suppose some samples(row) have "good" value combinations that are likely to yield similar output, whereas others have "bad" value combinations thus difficult to predict.</p>

<p>My goal is, by getting rid of of those bad samples which lack regularity, I want to improve final accuracy. Can someone tell me what could be the best algorithm or preprocess to automatically detect those samples so that only the good samples are going to be trained? Thank you in advance!</p>

<p>ENV: MXNet, R</p>