Data visualization is a compelling way to uncover hidden insights from the data and persuasively present them—every business benefits from making data easier to understand. Thus, visuals help to grasp large volumes of data instantly. Data Visualization makes insights more transparent, which helps in making decisions faster.
There are various visualization tools available for creating visualizations from datasets. However, these tools have a steep learning curve because specifications are needed to be made manually by codes. The requirement for the user to manually select among data columns and decide which statistical tool to apply for generating visuals can be a daunting task. Typical users with limited time, statistics and data visualization skills may get severe problems dealing with complex datasets.
Not all tools require the manual inputs of codes; most recommender systems operate on predetermined rules to automatically generate visualizations for the user to search and select. While effective for specific use cases, these rule-based approaches are costly and have limited scalability. In contrast, machine learning (ML)-based systems directly learn the relationship between data and visualizations by training models on analyst interaction or implicitly learning these rules from examples.
Here two ML models are described in detail that transforms the data into visualizations. One is a neural network model, and the other is a neural transformation model.
VizML model is a fully connected feedforward neural network (NN) with three hidden layers which consists of 1,000 neurons each with ReLU activation functions. It is implemented using PyTorch, which provides visualization recommendations after learning visualization choices from a large corpus of data. At first, five key design choices made by analysts while creating visualizations are identified. Then using one million dataset-visualization pairs collected from a popular online visualization platform, models are trained to predict these design choices. There is a higher accuracy as the neural network predicts the design choices rather than random guessing and simple classifiers.