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Statistics for Machine Learning

You're reading from  Statistics for Machine Learning

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781788295758
Pages 442 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Pratap Dangeti Pratap Dangeti
Profile icon Pratap Dangeti

Table of Contents (16) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Journey from Statistics to Machine Learning 2. Parallelism of Statistics and Machine Learning 3. Logistic Regression Versus Random Forest 4. Tree-Based Machine Learning Models 5. K-Nearest Neighbors and Naive Bayes 6. Support Vector Machines and Neural Networks 7. Recommendation Engines 8. Unsupervised Learning 9. Reinforcement Learning

KNN classifier with breast cancer Wisconsin data example


Breast cancer data has been utilized from the UCI machine learning repository http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29 for illustration purposes. Here the task is to find whether the cancer is malignant or benign based on various collected features such as clump thickness and so on using the KNN classifier:

# KNN Classifier - Breast Cancer 
>>> import numpy as np 
>>> import pandas as pd 
>>> from sklearn.metrics import accuracy_score,classification_report 
>>> breast_cancer = pd.read_csv("Breast_Cancer_Wisconsin.csv") 

The following are the first few rows to show how the data looks like. The Class value has class 2 and 4. Value 2 and 4 represent benign and malignant class, respectively. Whereas all the other variables do vary between value 1 and 10, which are very much categorical in nature:

Only the Bare_Nuclei variable has some missing values, here we are replacing...

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