Interpretation and visualization are built-in functions of the Python Toolkit. To make it easy to understand the results of an exploration or prediction activity, Flek includes a variety of components that enable the data scientist to:

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1. Plot probabilities using various diagrams: bar, scatter, pie, heatmap, etc.

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

3. Produce traceable predictions that associate to each score the rational and the rule used during classification.

4. Carry out validation tests to measure the accuracy of the prediction.

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Knowing how complex it is to work with probabilities the above 4 facilities are indispensable to any analytical activity. By providing a holistic approach to interpretation and visualization, Flek provides the means for AI citizens to tackle complex analytical tasks such as trying to: (i) visualize the distribution of one or more variables, (ii) discover relationships between variables, (iii) understanding the outcomes of a classification task (iv) interpreting the probability results of a mining activity.