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)


The bootstrap method essentially refers to drawing multiple samples (each known as a resample) from the dataset consisting of randomly chosen data points, where there can be an overlap in the data points contained in each resample and each data point has an equal probability of being selected from the overall dataset:

Figure 6.7: Randomly choosing data points

From the previous diagram, we can see that each of the five bootstrapped samples taken from the primary dataset is different and has different characteristics. As such, training models on each of these resamples would result in different predictions.

The following are the advantages of bootstrapping:

  • Each resample can contain different characteristics from that of the entire dataset, allowing us a different perspective of how the data behaves.
  • Algorithms that make use of bootstrapping are powerfully built and handle unseen data better, especially on smaller datasets that have...