Book Image

The Supervised Learning Workshop - Second Edition

By : Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur
Book Image

The Supervised Learning Workshop - Second Edition

By: Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

Overview of this book

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society. Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models. Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks. By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.
Table of Contents (9 chapters)

One-Hot Encoding

So, what is one-hot encoding? Well, in machine learning, we sometimes have categorical input features such as name, gender, and color. Such features contain label values rather than numeric values, such as John and Tom for name, male and female for gender, and red, blue, and green for color. Here, blue is one such label for the categorical feature – color. All machine learning models can work with numeric data, but many machine learning models cannot work with categorical data because of the way their underlying algorithms are designed. For example, decision trees can work with categorical data, but logistic regression cannot.

In order to still make use of categorical features with models such as logistic regression, we transform such features into a usable numeric format. Figure 6.1 shows an example of what this transformation looks like:

Figure 6.1: One-hot encoding

Figure 6.2 shows how one-hot encoding changes the dataset, once...