Data Visualization for Machine Learning

The most important techniques of Data Visualization for Machine Learning.

As part of any machine learning task, data visualization plays an important role in learning more about the available data and in identifying any major patterns. In this article, I’ll walk you through the most important techniques of data visualization for machine learning that you need to know when working in a professional environment.

Here I will cover some important techniques that could help us meet the challenges of data visualization for machine learning, such as parallel coordinate plots, summary data tables, drawing ANN charts and many more.

Also, Read – Machine Learning Full Course For free.

Data Preparation

Before getting into the task of data visualization for machine learning let’s prepare our data for this task. I will start by importing some necessary libraries that we will need in the process:

Now, I will prepare the data by identifying and removing the missing values, and then I will create a new data frame so that we can easily continue with the task of data visualization for machine learning.

You can download the data that I am using in this task from here:

data visualization

Now our data is ready to be used in the task of techniques of data visualization for machine learning.

Techniques of Data Visualization for Machine Learning

Hyperparameter Optimization:

One of the most common activities in machine learning is hyperparameter optimization. Tuning machine learning models is one type of optimization problem. We have a set of hyperparameters and we are looking to find the right combination of their values ​​that can help us find the minimum or the maximum of a function.

One of the best solutions for this type of task is to use a parallel coordinate plot. By using this type of graph, we can easily compare different variables together to discover possible relationships.

In the case of hyperparameter optimization, this can be used as a simple tool to inspect which combination of parameters can give us the highest test accuracy. Another possible use of parallel coordinate plots in data analysis is to inspect relationships in values ​​between different entities in a data frame:

Hyperparameter Optimization:

Plotly Prediction Table:

When working with time-series data in machine learning, sometimes it can be really handy to be able to quickly understand which data points our model is performing poorly, to try and understand the limitations it might face.

One possible approach is to create a summary table with actual and predicted values and some form of metric summarizing how well a data point has been predicted. Using Plotly, this can be easily done by creating a plot function:

data visualization for machine learning Plotly Prediction Table:

Decision Trees:

Decision trees are one of the most easily explained types of machine learning models. Thanks to their basilar structure, it is easily possible to examine how the algorithm decides to make its decision by looking at the conditions on the different branches of the tree.

Additionally, decision trees can also be used as a feature selection technique, as the algorithm places features at higher levels of the tree that are most useful for our desired classification/regression tasks. In this way, the features at the bottom of the tree could be ignored because they contain less information:

Decision Trees:

In the figure above, the different classes are represented by a different colour. The entity distributions of all the different classes are represented in the starting node of the tree.

As we move down each branch, the algorithm then tries to better separate the different distributions using the function described under each of the node graphs.

The circles generated next to the distributions represent the number of elements correctly classified after following a certain node, the greater the number of elements, the larger the size of the circle.

Decision Boundaries:

Decision limits are one of the simplest approaches to graphically understanding how a machine learning model makes its predictions. One of the easiest ways to plot decision boundaries in Python is to use Mlxtend.

This library can, in fact, be used to trace the decision boundaries of machine learning and deep learning models. Let’s see how to draw a decision boundary:

data visualization for machine learning Decision Boundaries:

Artificial Neural Networks:

Another technique of data visualization for machine learning that can be very useful when creating new neural network architectures is to visualize their structure. This can be easily done using the ANN Visualiser:

Artificial Neural Networks:

So these were the most important techniques of data visualization for machine learning.

I hope you liked this article on the most important techniques of data visualization for machine learning that you need to know while working in a professional environment. Feel free to ask your valuable questions in the comments section below.

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