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Scikit-Learn Cookbook, by Trent Hauck
Ebook Scikit-Learn Cookbook, by Trent Hauck
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Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluationAbout This Book
- Learn how to handle a variety of tasks with Scikit-Learn with interesting recipes that show you how the library really works
- Use Scikit-Learn to simplify the programming side data so you can focus on thinking
- Discover how to apply algorithms in a variety of situations
If you're a data scientist already familiar with Python but not Scikit-Learn, or are familiar with other programming languages like R and want to take the plunge with the gold standard of Python machine learning libraries, then this is the book for you.
What You Will Learn- Address algorithms of various levels of complexity and learn how to analyze data at the same time
- Handle common data problems such as feature extraction and missing data
- Understand how to evaluate your models against themselves and any other model
- Discover just enough math needed to learn how to think about the connections between various algorithms
- Customize the machine learning algorithm to fit your problem, and learn how to modify it when the situation calls for it
- Incorporate other packages from the Python ecosystem to munge and visualize your dataset
Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. Its consistent API and plethora of features help solve any machine learning problem it comes across.
The book starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets.
- Sales Rank: #1354942 in Books
- Published on: 2014-11-03
- Released on: 2014-11-04
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .49" w x 7.50" l, .83 pounds
- Binding: Paperback
- 214 pages
About the Author
Trent Hauck
Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.
Most helpful customer reviews
5 of 5 people found the following review helpful.
Potential lost
By Abhijit Dasgupta
I recently got Trent Hauck's book "scikit-learn Cookbook", and am posting a short review of it.
First of all I'd like to point out that this book isn't for beginners or new entrants to Data Science. The book assumes an intermediate level of sophistication in terms of methods covered and statistical/computer science algorithms presented. Without some background this book will be very difficult.
The book is split into 5 chapters, from Premodel Workflow to Postmodel Workflow, with intervening chapters on regression, clustering and classification. The book is rather dense, and covers a lot of material. The later chapters are smoother in style than the earlier chapters, making for easier reading.
However there is a lot of code, and the code isn't necessarily encapsulated within each recipe, so it was sometimes hard to replicate the books code without searching for required code from earlier recipes. Also, since some algorithms are random in nature, the author neglects to explicitly set the random number seed, and so there is no way for the reader to exactly replicate the results. If this doesn't bother you, and you just want to see how the process works, fine. There is also rather haphazard introduction to packages outside of scikit-learn, like pandas, patsy, scipy, and others. This makes the discourse confusing in places and doesn't provide a straightforward recipe for the problem at hand. It is good that other packages and choices are mentioned, but they need to be less prominent to make the reading easier.
The editing of this book leaves something to be desired. There are typos, of course. The fact that Guassian processes are introduced in Chapter 1 even before regression, while the text for that recipe refers to the regression recipes as "as we have already seen" is a huge gaffe. Gaussian process regression thematically does not belong in Chapter 1 at all!! Better editing and proofing could have made this book much better.
One minor peeve. In talking about scaling variables, the author refers to a standard normal distribution. What he achieves in that recipe is just mean-variance standardization, and has nothing to do with the normal distribution. Just doing mean-variance standardization does not ensure normality in any way, so the premise of that recipe is misleading. Mean-variance standardization can be a good thing. but doesn't account for skewness and other characteristics in the data distribution that would continue to look non-normal.
Overall, I think there is lot of good material in the book if you go looking for it, but the organization and editing deficiencies make it, in my opinion, a case of potential lost. This book could have been much better.
0 of 0 people found the following review helpful.
Disappointing
By GEORGE R. FISHER
Most scikit learn books are 'introductory', this one holds out the hope of being written for people who know what sklearn is and it does achieve that. However, it is very poorly written; it looks like it was written in a great rush to get published and that greatly detracts from its value. By and large, YouTube is a better source.
1 of 1 people found the following review helpful.
Have it close to your mouse! Most likely you'll find find the recipe you need for your specific problem!
By Roberto Avilés
'Over 50 recipes (I’ve counted 54!) to incorporate into every step of the data science pipeline, from feature extraction to model building and evaluation’. Could we ask more? Trent Hauck holds degrees in Accountancy and Finance but, he knows about Pydata, Pandas, IPython and Google analytics. That is the origin of this book: experience.
Let’s start with a caveat. Other books invest a whole chapter describing the tools you need and how to install it. Strategy here is different. In the preface the author list the tools & versions we will need plus, every recipe is structured in, the following, sections: ‘Getting ready’ (what to expect in the recipe and how to set up the software or settings requested for this recipe), ‘How to do it’ (the steps to cook the recipe), ‘How it works’ (a summary of what happened in previous section), ‘There’s more’ (additional information about the recipe, just to open our appetite), and finally, ‘See also’ (references to the recipe.) The preface summarizes, also, basic conventions followed in the book. However (another caveat) I believe the author presumes the reader either has set scikit-learn or will be able to set it correctly. If not your case, have in mind that ALL the essential information is in scikit-learn.org. Do not be afraid to do questions in places as ‘stackoverflow’, you know, ‘whistle while you type…and everything will work!’
Now, Chapter 1 is about ‘Premodel Workflow’: how to prepare a dataset for modeling and how scikit-learn helps us to improve the preprocessing steps.
Chapter 2, ‘Working with Linear Models’: identify problems for which a linear model is acceptable, introduces the most used models.
Chapter 3, ‘Building Models with Distance Metrics’: topics that arise when looking for the similarity between data sets.
Chapter 4, ‘Classifying with scikit-learn’: methods used to find if a data point is member of a class.
Finally in Chapter 5, ‘Postmodel Workflow’: pick a basic model obtained from a recipe and tune it to get and even better result.
Let ‘s see a bit more, the first recipe in Chapter 1. As a starting tip, the author explains the book was written using scikit-learn0.15, NumPy 1.9 and pandas 0.13. A short summary on the first recipe, ‘Getting sample data from external sources’, is a hands on exercise 2 pages long. ‘Getting ready’ “, how to get and load the datasets. ‘How to do it’, how to load datasets, inspect its contents and fetch a specific portion of the dataset. Then, at ‘How it works’, the author explains us what happen when a dataset is loaded and becomes a “dictionary”. In ‘There’s more’, we learn what happens with external datasets, how to configure that behavior and how to check the default location. Finally, in ‘See also’ we are told the UCI Machine Learning Repository is the place to find many sample datasets, other places being government agencies, Kaggle competitions and KDD.
Summarizing, this is a must read for everyone interested in Machine Learning and particularly, on the use of a open tool as ‘scikit-learn’. Must likely there is one or more recipe that fits your particular interests and needs. A great book, something we must have in our desk, close to the mouse.
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