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)


In the previous chapters, we discussed the two types of supervised learning problems: regression and classification. We looked at a number of algorithms for each type and delved into how those algorithms worked.

But there are times when these algorithms, no matter how complex they are, just don't seem to perform well on the data that we have. There could be a variety of causes and reasons for this – perhaps the data is not good enough, perhaps there really is no trend where we are trying to find one, or perhaps the model itself is too complex.

Wait. What?! How can a model being too complex be a problem? If a model is too complex and there isn't enough data, the model could fit so well to the data that it learns even the noise and outliers, which is not what we want.

Often, where a single complex algorithm can give us a result that is way off from actual results, aggregating the results from a group of models can give us a result that&apos...