Tuesday, January 23, 2018

Scikit Learn API principles paper



The Scikit-Learn API is designed with the following guiding principles in mind, as outlined in the Scikit-Learn API paper:
  • Consistency: All objects share a common interface drawn from a limited set of methods, with consistent documentation.
  • Inspection: All specified parameter values are exposed as public attributes.
  • Limited object hierarchy: Only algorithms are represented by Python classes; datasets are represented in standard formats (NumPy arrays, Pandas DataFrames, SciPy sparse matrices) and parameter names use standard Python strings.
  • Composition: Many machine learning tasks can be expressed as sequences of more fundamental algorithms, and Scikit-Learn makes use of this wherever possible.
  • Sensible defaults: When models require user-specified parameters, the library defines an appropriate default value.

Roger Peng: The Art of Data Science: “A Guide for Anyone Who Works with Data”

Another excellent read. Short and to the point with many useful tips.

Roger Peng: The Art of Data Science: “A Guide for Anyone Who Works with Data”

Get it for free on leanpub.

למתעניינים ב-Data Science ושוקלים ללמוד להיות מדעני נתונים או לאלה שכבר עוסקים בתחום אני ממליץ מאד על הספר של רוג'ר פנג:


שלמה
mathematic.ai

The Elements of Data Analytic Style: “A guide for people who want to analyze data


Excellent read. Short and to the point. Many useful tips.

Jeff Leek: The Elements of Data Analytic Style: “A guide for people who want to analyze data.

Get it for free at leanpub



למתעניינים ב-Data Science ושוקלים ללמוד להיות מדעני נתונים או לאלה שכבר עוסקים בתחום אני ממליץ מאד על הספר של ג'ף ליק:


שלמה
mathematic.ai