Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying The Data Science Workshop
  • Table Of Contents Toc
  • Feedback & Rating feedback
The Data Science Workshop

The Data Science Workshop

By : Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare
3 (2)
close
close
The Data Science Workshop

The Data Science Workshop

3 (2)
By: Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare

Overview of this book

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.
Table of Contents (16 chapters)
close
close
Preface
12
12. Feature Engineering

Minimum Sample in Leaf

Previously, we learned how to reduce or increase the depth of trees in Random Forest and saw how it can affect its performance and tendency to overfit or not. Now we will go through another important hyperparameter: min_samples_leaf.

This hyperparameter, as its name implies, is related to the leaf nodes of the trees. We saw earlier that the RandomForest algorithm builds nodes that will clearly separate observations into two different groups. If we look at the tree example in Figure 4.15, the top node is splitting data into two groups: the left-hand group contains mainly observations for the bending_1 class and the right-hand group can be from any class. This sounds like a reasonable split but are we sure it is not increasing the risk of overfitting? For instance, what if this split leads to only one observation falling on the left-hand side? This rule would be very specific (applying to only one single case) and we can't say it is generic enough for unseen...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY