Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
The Data Science Workshop

You're reading from   The Data Science Workshop A New, Interactive Approach to Learning Data Science

Arrow left icon
Product type Paperback
Published in Jan 2020
Publisher
ISBN-13 9781838981266
Length 818 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (5):
Arrow left icon
Thomas Joseph Thomas Joseph
Author Profile Icon Thomas Joseph
Thomas Joseph
Andrew Worsley Andrew Worsley
Author Profile Icon Andrew Worsley
Andrew Worsley
Robert Thas John Robert Thas John
Author Profile Icon Robert Thas John
Robert Thas John
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Dr. Samuel Asare Dr. Samuel Asare
Author Profile Icon Dr. Samuel Asare
Dr. Samuel Asare
+1 more Show less
Arrow right icon
View More author details
Toc

Table of Contents (18) Chapters Close

Preface 1. Introduction to Data Science in Python 2. Regression FREE CHAPTER 3. Binary Classification 4. Multiclass Classification with RandomForest 5. Performing Your First Cluster Analysis 6. How to Assess Performance 7. The Generalization of Machine Learning Models 8. Hyperparameter Tuning 9. Interpreting a Machine Learning Model 10. Analyzing a Dataset 11. Data Preparation 12. Feature Engineering 13. Imbalanced Datasets 14. Dimensionality Reduction 15. Ensemble Learning 16. Machine Learning Pipelines 17. Automated Feature Engineering

Partial Dependence Plots

Another tool that is model-agnostic is a partial dependence plot. It is a visual tool for analyzing the effect of a feature on the target variable. To achieve this, we can plot the values of the feature we are interested in analyzing on the x-axis and the target variable on the y-axis and then show all the observations from the dataset on this graph. Let's try it on the Breast Cancer dataset from sklearn:

from sklearn.datasets import load_breast_cancer
import pandas as pd
data = load_breast_cancer()
df = pd.DataFrame(data.data, columns=data.feature_names)

Now that we have loaded the data and converted it to a DataFrame, let's have a look at the worst concave points column:

import altair as alt
alt.Chart(df).mark_circle(size=60).encode(
    x='worst concave points',
    y='target'
)

Figure 9.28: Scatter plot of the worst concave points and target variables

From this plot, we can see:

  • Most...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime