Publisher's Synopsis
This book develops Nonparametric Techniques for Prediction and Clasification: Nearest Neighbors, KNN Classifier, Ensemble Learning, Classification Ensemble, Regression Ensemble, Boosting, Bagging, Bagging of Regression Trees, Bagging of Classification Trees, Quantile Regression, Random Forest and Support Vector Machines for Binary Classification. This techniques are very important for work in Data Science. In addition, the book also develops examples and applications relating to such methods. Nonparametric Techniques splits into two broad categories: classification and regression. - In classification, the goal is to assign a class (or label) from a finite set of classes to an observation. That is, responses are categorical variables. Applications include spam filters, advertisement recommendation systems, and image and speech recognition. Predicting whether a patient will have a heart attack within a year is a classification problem, and the possible classes are true and false. Classification algorithms usually apply to nominal response values. However, some algorithms can accommodate ordinal classes (see fitcecoc). - In regression, the goal is to predict a continuous measurement for an observation. That is, the responses variables are real numbers. Applications include forecasting stock prices, energy consumption, or disease incidence. The most important content in this book is the following: - "Classification Using Nearest Neighbors" - "Framework for Ensemble Learning" - "Ensemble Algorithms" - "Train Classification Ensemble" - "Train Regression Ensemble" - "Select Predictors for Random Forests" - "Test Ensemble Quality" - "Ensemble Regularization" - "Classification with Imbalanced Data" - "Handle Imbalanced Data or Unequal Misclassification Costs in Classification Ensembles" - "Classification with Many Categorical Levels" - "Surrogate Splits" - "LPBoost and TotalBoost for Small Ensembles" - "Tune RobustBoost" - "Random Subspace Classification" - "Bootstrap Aggregation (Bagging) of Regression Trees" - "Bootstrap Aggregation (Bagging) of Classification Trees" - "Detect Outliers Using Quantile Regression" - "Conditional Quantile Estimation Using Kernel Smoothing" - "Tune Random Forest Using Quantile Error and Bayesian Optimization" - "Support Vector Machines for Binary Classification"
