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Mastering predictive analytics with R : machine learning techniques for advanced models /
Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts About This Book Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding Leveraging the flexibility and modula...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
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| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover ; Copyright ; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Gearing Up for Predictive Modeling ; Models; Learning from data; The core components of a model; Our first model
- k-nearest neighbors; Types of model; Supervised, unsupervised, semi-supervised, and reinforcement learning models; Parametric and nonparametric models; Regression and classification models; Real-time and batch machine learning models; The process of predictive modeling; Defining the model's objective; Collecting the data; Picking a model.
- Pre-processing the dataExploratory data analysis; Feature transformations; Encoding categorical features; Missing data; Outliers; Removing problematic features; Feature engineering and dimensionality reduction; Training and assessing the model; Repeating with different models and final model selection; Deploying the model; Summary; Chapter 2: Tidying Data and Measuring Performance ; Getting started; Tidying data; Categorizing data quality; The first step; The next step; The final step; Performance metrics; Assessing regression models; Assessing classification models.
- Assessing binary classification modelsCross-validation; Learning curves; Plot and ping; Summary; Chapter 3: Linear Regression ; Introduction to linear regression; Assumptions of linear regression; Simple linear regression; Estimating the regression coefficients; Multiple linear regression; Predicting CPU performance; Predicting the price of used cars; Assessing linear regression models; Residual analysis; Significance tests for linear regression; Performance metrics for linear regression; Comparing different regression models; Test set performance; Problems with linear regression.
- MulticollinearityOutliers; Feature selection; Regularization; Ridge regression; Least absolute shrinkage and selection operator (lasso); Implementing regularization in R; Polynomial regression; Summary; Chapter 4: Generalized Linear Models ; Classifying with linear regression; Introduction to logistic regression; Generalized linear models; Interpreting coefficients in logistic regression; Assumptions of logistic regression; Maximum likelihood estimation; Predicting heart disease; Assessing logistic regression models; Model deviance; Test set performance; Regularization with the lasso.
- Classification metricsExtensions of the binary logistic classifier; Multinomial logistic regression; Predicting glass type; Ordinal logistic regression; Predicting wine quality; Poisson regression; Negative Binomial regression; Summary; Chapter 5: Neural Networks ; The biological neuron; The artificial neuron; Stochastic gradient descent; Gradient descent and local minima; The perceptron algorithm; Linear separation; The logistic neuron; Multilayer perceptron networks; Training multilayer perceptron networks; The back propagation algorithm; Predicting the energy efficiency of buildings.