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)

Performance Improvement Tactics

Performance improvement for supervised machine learning models is an iterative process, and a continuous cycle of updating and evaluation is usually required to get the perfect model. While the previous sections in this chapter dealt with the evaluation strategies, this section will talk about model updating: we will discuss some ways we can determine what our model needs to give it that performance boost, and how to effect that change in our model.

Variation in Train and Test Errors

In the previous chapter, we introduced the concepts of underfitting and overfitting, and mentioned a few ways to overcome them, later introducing ensemble models. But we didn't talk about how to identify whether our model was underfitting or overfitting to the training data.

It's usually useful to look at the learning and validation curves.

Learning Curve

The learning curve shows the variation in the training and validation errors with the training...