<p>In this episode, I talk about different techniques that we can use to predict the outcome of some question depending on input features.</p>
<p>The different techniques I will go through are the ZeroR and OneR that will create a baseline for the rest of the methods.</p>
<p>Next up, we have the Naive Bayes classifier that is simple but powerful for some applications.</p>
<p>Nearest neighbor and Decision trees are next up that requires more training but is very efficient when you infer results.</p>
<p>Multi-layer perceptron (MLP) is the first technique that is close to the ones we usually see in Machine Learning frameworks used today. But it is just a precursor to Convolutional Neural Network (CNN) because of the size requirements. MLPs have the same size for all the hidden layers, which makes it unfeasible for larger networks.</p>
<p>CNNs, on the other hand, uses subsampling that will shrink the layer maps to reduce the size of the network without reducing the accuracy of the predictions.</p>
<h2 id="links">Links</h2>
<ul>
<li>Some references for further reading on Wikipedia.
<ul>
<li><a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier" class="uri">https://en.wikipedia.org/wiki/Naive_Bayes_classifier</a></li>
<li><a href="https://en.wikipedia.org/wiki/Nearest_neighbor_search" class="uri">https://en.wikipedia.org/wiki/Nearest_neighbor_search</a></li>
<li><a href="https://en.wikipedia.org/wiki/Decision_tree" class="uri">https://en.wikipedia.org/wiki/Decision_tree</a></li>
<li><a href="https://en.wikipedia.org/wiki/Support-vector_machine" class="uri">https://en.wikipedia.org/wiki/Support-vector_machine</a></li>
<li><a href="https://en.wikipedia.org/wiki/Multilayer_perceptron" class="uri">https://en.wikipedia.org/wiki/Multilayer_perceptron</a></li>
<li><a href="https://en.wikipedia.org/wiki/Convolutional_neural_network" class="uri">https://en.wikipedia.org/wiki/Convolutional_neural_network</a></li>
</ul></li>
<li>A video I made some years ago where you can see some visual aids for this subject.<br />
<a href="https://youtu.be/Xys1N_7MbSs" class="uri">https://youtu.be/Xys1N_7MbSs</a></li>
</ul>