Behind every successful ML project is the ability to explain and interpret outcomes and visualize results.
Interpret & Visualize
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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Plot probabilities using various diagrams: bar, scatter, pie, heatmap, etc.
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Generate a meaningful English statements for each Nugget or Probability stored in the probabilistic model.
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Produce traceable predictions that associate to each score the rational and the rule used during classification.
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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.