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Statistics for Machine Learning

You're reading from   Statistics for Machine Learning Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781788295758
Length 442 pages
Edition 1st Edition
Languages
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Author (1):
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Pratap Dangeti Pratap Dangeti
Author Profile Icon Pratap Dangeti
Pratap Dangeti
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Table of Contents (10) Chapters Close

Preface 1. Journey from Statistics to Machine Learning FREE CHAPTER 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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