We defined machine learning as the design and study of systems that learn from experience to improve their performance of a task as measured by some metric. K-means is an unsupervised learning algorithm; there are no labels or ground truth to compare with the clusters. However, we can still evaluate the performance of the algorithm using intrinsic measures. We have already discussed measuring the distortions of clusters. In this section, we will discuss another performance measure for clustering called silhouette coefficient. The silhouette coefficient is a measure of compactness and separation of clusters. It increases as the quality of clusters increases; it is large for compact clusters that are far from each other and small for large, overlapping clusters. The silhouette coefficient is calculated per instance; for a set of instances, it is calculated as...
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Book Overview & Buying
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Table Of Contents
Mastering Machine Learning with scikit-learn - Second Edition
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Mastering Machine Learning with scikit-learn
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Overview of this book
Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model.
This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance.
By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.
Table of Contents (15 chapters)
Preface
Free Chapter
The Fundamentals of Machine Learning
Simple Linear Regression
Classification and Regression with k-Nearest Neighbors
Feature Extraction
From Simple Linear Regression to Multiple Linear Regression
From Linear Regression to Logistic Regression
Naive Bayes
Nonlinear Classification and Regression with Decision Trees
From Decision Trees to Random Forests and Other Ensemble Methods
The Perceptron
From the Perceptron to Support Vector Machines
From the Perceptron to Artificial Neural Networks
K-means
Dimensionality Reduction with Principal Component Analysis
