This documentation is for Machine Learner 1.1.0. View documentation for the latest release.

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  1. Log in to the WSO2 ML UI, if you are not already logged in. 
  2. Click Datasets button as shown below.
  3. Click the EXPLORE button of the dataset which you want to explore as shown below.
    click explore button
    You view different perspectives on the dataset through four chart types as follows. 

    Scatter plot & histogram


    Scatter plot visualizes the relationship between the two selected features of the dataset. Moreover, histograms provide the user a graphical representation of the data distribution for the same two features you select. The scatter plot user interface allows you to select two numerical features from the dataset to be visualized through a scatter plot and histograms.

    Parallel set

    parallel set

    Parallel set is a visualization method used for categorical data. It adopts the layout of parallel coordinates, but substitutes the individual data points by a frequency-based representation. This abstract view is combined with a set of interactions. It supports visual data analysis of large and complex data sets. Using the parallel sets user interface, you can specify which categorical features to draw the diagram. 

    Trellis chart

     trellis chart

    Trellis chart is a series of graphs or charts based on the same scale and axes, allowing them to be easily compared. It uses multiple views to show different partitions of a dataset, and is useful for finding the structure and patterns in complex data. Trellis chart user interface allows you to select one categorical feature and multiple numerical features (bound to a maximum) to draw the diagram.

    Cluster diagramdiagram 

    Cluster Diagram
    Cluster Diagram

    cluster diagram

    Cluster diagram is a general type of diagram, which depicts one or more clusters in a dataset. A cluster in general is a group or collection of discrete points that are close to each other. In explore view, a cluster diagram provides a perspective on data clusters for two selected numerical features. A popular clustering algorithm is applied on the data sample to derive data clusters. You can select two numerical features and the number of clusters required through the cluster diagram user interface.