Building Your First Classification Model in Python with Scikit-learn

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Description

Summarizing, we can conclude that, today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration and, in particular, model explanation (obtaining insight into model-based predictions) and model examination (evaluation of model’s performance and understanding the weaknesses). Thus, in this book, we present a collection of methods that may be used for this purpose. As development of such methods is a very active area of research, with new methods becoming available almost on a continuous basis, we do not aim at being exhaustive. Rather, we present the mindset, key concepts and issues, and several examples of methods that can be used in model exploration.Read more.

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The techniques and tools covered in Explanatory Model Analysis are most similar to the requirements found in Data Scientist job advertisements.

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