Book Image

Python Data Science Essentials - Third Edition

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials - Third Edition

By: Alberto Boschetti, Luca Massaron

Overview of this book

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users
Table of Contents (11 chapters)

Validation metrics

In order to evaluate the performance of the data science system that you have built and check how close you are to the goal 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 and how they are used by machine learning algorithms.

Learning how to choose the right score/error measure for your data science project is really a matter of experience. We found it very helpful to consult (and participate in) the data science competitions held by Kaggle (kaggle.com), a company devoted to organizing data challenges between data scientists from all over the world. By observing the various challenges and what score or error measure...