Book Image

Clojure for Data Science

By : Garner
Book Image

Clojure for Data Science

By: Garner

Overview of this book

The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a scripting language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs. Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility! You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models. Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future.
Table of Contents (12 chapters)
11
Index

Goodness-of-fit and R-square


Although we can see from the residual plot that a linear model is a good fit for our data, it would be desirable to quantify just how good it is. Also called the coefficient of determination, R2 varies between zero and one and indicates the explanatory power of the linear regression model. It calculates the proportion of variation in the dependent variable explained, or accounted for, by the independent variable.

Generally, the closer R2 is to 1, the better the regression line fits the points and the more the variation in Y is explained by X. R2 can be calculated using the following formula:

Here, var(ε) is the variance of the residuals and var(Y) is the variance in Y. To understand what this means, let's suppose you're trying to guess someone's weight. If you don't know anything else about them, your best strategy would be to guess the mean of the weights within the population in general. This way, the mean squared error of your guess compared to their true weight...