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)

Overfitting and Underfitting

Let's say we fit a supervised learning algorithm to our data and subsequently use the model to perform a prediction on a hold-out validation set. The performance of this model will be considered to be good based on how well it generalizes, that is, how well it makes predictions for data points in an independent validation dataset.

Sometimes, we find that the model is not able to make accurate predictions and gives poor performance on the validation data. This poor performance can be the result of a model that is too simple to model the data appropriately, or a model that is too complex to generalize to the validation dataset. In the former case, the model has a high bias and results in underfitting, while, in the latter case, the model has a high variance and results in overfitting.


The bias in the prediction of a machine learning model represents the difference between the predicted target value and the true target value of a data point...