KNN is a lazy learner. Also known as instance-based learners, lazy learners simply store the training dataset with little or no processing. In contrast to eager learners such as simple linear regression, KNN does not estimate the parameters of a model that generalizes the training data during a training phase. Lazy learning has advantages and disadvantages. Training an eager learner is often computationally costly, but prediction with the resulting model is often inexpensive. For simple linear regression, prediction consists only of multiplying the learned coefficient by the feature, and adding the learned intercept parameter. A lazy learner can predict almost immediately, but making predictions can be costly. In the simplest implementation of KNN, prediction requires calculating the distances between a test instance and all training instances...
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Mastering Machine Learning with scikit-learn
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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
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