Machine Learning Visualization

Machine Learning Visualization

A collection of a few interesting techniques which can be used in order to visualise different aspects of the Machine Learning pipeline. In this article, we are going to explore some techniques which could help us to face this challenge such as: Parallel Coordinates Plots, Summary Data Tables, drawing ANNs graphs and many more.

Introduction

As part of any Data Science project, Data Visualization plays an important part in order to learn more about the available data and to identify any main pattern.

Wouldn’t be great if it could be possible to make as visually intuitive as possible also the Machine Learning part of the analysis?

In this article, we are going to explore some techniques which could help us to face this challenge such as: Parallel Coordinates Plots, Summary Data Tables, drawing ANNs graphs and many more.

All the code used in this article is freely available on my Github and Kaggle Accounts.

Techniques

Hyperparameters Optimization

Hyperparameter Optimization is one of the most common activities in Machine/Deep Learning. Machine Learning models tuning is a type of optimization problem. We have a set of hyperparameters (eg. learning rate, number of hidden units, etc…) and we aim to find out the right combination of their values which can help us to find either the minimum (eg. loss) or the maximum (eg. accuracy) of a function.

In one of my previous articles, I went into the details of how what kind of techniques we can use in this ambit and how to test them in a 3D space, in this article I will instead show you how we can accomplish that for reporting in a 2D space.

One of the best solutions for this type of task is to use a parallel coordinates plot (Figure 1). Using this type of plot, we can in fact easily compare different variables (eg. features) together in order to discover possible relationships. In the case of Hyperparameters Optimization, this can be used as a simple tool to inspect what combination of parameters can give us the greatest test accuracy. Another possible use of parallel coordinates plots in Data Analysis is to inspect relationships in values between the different features in a data frame.

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