Abstract: A visualization is presented while tuning a machine learning model. A model tuning process writes tuning data to a history table. The model tuning process is repeatedly training and scoring a model type with different sets of values of hyperparameters defined based on the model type. An objective function value is computed for each set of values of the hyperparameters. Data stored in the history table is accessed and used to identify the hyperparameters. (A) A page template is selected from page templates that describe graphical objects presented in the display. (B) The page template is updated with the accessed data. (C) The display is updated using the updated page template. (D) At the end of a refresh time period, new data stored in the history table by the model tuning process is accessed. (E) (B) through (D) are repeated with the accessed data replaced with the accessed new data.

Abstract: Various embodiments are generally directed to techniques for visualizing clustered datasets, such as by utilizing multiple colors and multiple color gradients to represent data from different clustered datasets, for instance. Some embodiments are particularly directed to using different colors associated with each cluster of data to visualize which cluster is dominant in each cell of a heat map. Further, in many embodiments, a color gradient may be used among different heat map cells of a common color that correspond to a common cluster to visualize data distributions within each cluster of data represented in the heat map. In multiple embodiments, colors and color gradients may be utilized in conjunction with visualizing clustered datasets to enable identification of useful patterns and relationships among a collection of clustered datasets. In several embodiments, heat maps and/or heat map matrices may be generated and presented via a user interface (UI).