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Behind every successful ML project is the ability to explain results and visualize outcomes.

Interpret & Visualize

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Interpretation and visualization are built-in functions of the Python Toolkit, which make it easy to explain and understand results of auto-discovery, exploration, mining or prediction activity. 

Knowing the complexity of probabilistic analysis, Flek offer 5 facilities that are indispensable to explaining results, namely:

  1. Visualize probabilities by plotting distributions using bar, scatter, pie and heatmap charts.

  2. Generate meaningful English Statements for each Nugget or Probability stored in the probabilistic model.

  3. Produce traceable reports that associate to a given score the rules or computation used during discovery, prediction or recommendation..

  4. Validate tests by measuring the accuracy of the prediction or classification operation.

  5. Generate detailed or summary descriptions of the auto-discovery result in order to comprehend relationship between variables or features.

Now, with all these tools in hand, AI citizens are better equipped to tackle complex mining tasks and interpret their AI activity with ease.

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