Book Image

Python Data Science Essentials

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials

By: Alberto Boschetti, Luca Massaron

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Table of Contents (13 chapters)

Scoring functions

In order to evaluate the performance of the system and check how close you are to the objective that you have in mind, you need to use a function that scores the outcome. Typically, different scoring functions are used to deal with binary classification, multilabel classification, regression, or a clustering problem. Now, let's see the most popular functions for each of these tasks.

Multilabel classification

When your task is to predict more than a single label (for instance, what's the weather like today? Which flower is this? What's your job?), it's called a multilabel classification. This is a very popular task, and many performance metrics exist to evaluate classifiers. Of course, you can use all these measures in the case of binary classification. Now, let's explain them with a simple real-world example:

In: from sklearn import datasets
iris = datasets.load_iris()
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split...