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

Bias and variance


Overfitting is a problem that occurs with machine learning algorithms that are able to generate very accurate results on a training dataset but fail to generalize very well from what they've learned. We say that models which have overfit the data have very high variance. When we trained our decision tree on data that included the numeric age of passengers, we were overfitting the data.

Conversely, certain models may have very high bias. This is a situation where the model has a strong tendency towards a certain outcome irrespective of the training examples to the contrary. Recall our example of a classifier that always predicts that a survivor will perish. This classifier would perform well on dataset with low survivor rates, but very poorly otherwise.

In the case of high bias, the model is unlikely to perform well on diverse inputs at the training stage. In the case of high variance, the model is unlikely to perform well on data that differs from that which it was trained...