Model interpretation and visualization are built-in functions of the Toolkit library. To make it easy to explore results and understand what is going inside the machine, 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 tests using various prediction error or accuracy metrics

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Knowing how complex it is to work with probabilities these 4 techniques are indispensable when trying to: (i) discover relationships between variables, (ii) solve classification problems or (iii) explore probabilistic results.

 

​By providing a holistic approach to interpretation and visualization, Flek opens a new door for data scientists and programmers alike to tackle complex machine learning problems from all sides.