book

Mastering Predictive Analytics with R

by Rui Miguel Forte

June 2015

Beginner to intermediate

414 pages

11h 26m

English

Packt Publishing

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What this book covers

Downloading the example codeErrataPiracyQuestions
ModelsLearning from dataThe core components of a modelOur first model: k-nearest neighbors
Supervised, unsupervised, semi-supervised, and reinforcement learning modelsParametric and nonparametric modelsRegression and classification modelsReal-time and batch machine learning models
Defining the model's objectiveCollecting the dataPicking a modelPreprocessing the dataExploratory data analysisFeature transformationsEncoding categorical featuresMissing dataOutliersRemoving problematic featuresFeature engineering and dimensionality reductionTraining and assessing the modelRepeating with different models and final model selectionDeploying the model
Assessing regression modelsAssessing classification modelsAssessing binary classification models
Introduction to linear regressionAssumptions of linear regression
Estimating the regression coefficients
Predicting CPU performancePredicting the price of used cars
Residual analysisSignificance tests for linear regressionPerformance metrics for linear regressionComparing different regression modelsTest set performance
MulticollinearityOutliers
Ridge regressionLeast absolute shrinkage and selection operator (lasso)Implementing regularization in R
Classifying with linear regression
Generalized linear modelsInterpreting coefficients in logistic regressionAssumptions of logistic regressionMaximum likelihood estimation
Model devianceTest set performance
Multinomial logistic regressionPredicting glass typeOrdinal logistic regressionPredicting wine quality
The biological neuron
Gradient descent and local minimaThe perceptron algorithmLinear separationThe logistic neuron
Training multilayer perceptron networks
Evaluating multilayer perceptrons for regression
Receiver operating characteristic curves
Maximal margin classification
Inner products
The intuition for tree models
Classification and regression treesCART regression treesTree pruningMissing dataRegression model treesCART classification treesC5.0
Tuning model parameters in CART treesVariable importance in tree modelsRegression model trees in action
BaggingMargins and out-of-bag observationsPredicting complex skill learning with baggingPredicting heart disease with baggingLimitations of bagging
AdaBoost
Limitations of boosting
The importance of variables in random forests
A little graph theory
Predicting the sentiment of movie reviews
Fundamental concepts of time seriesTime series summary functions
White noiseFitting a white noise time seriesRandom walkFitting a random walk
Moving average modelsAutoregressive modelsAutoregressive moving average models
Autoregressive integrated moving average modelsAutoregressive conditional heteroscedasticity modelsGeneralized autoregressive heteroscedasticity models
An overview of topic modeling
The Dirichlet distributionThe generative processFitting an LDA model
Model stabilityFinding the number of topicsTopic distributionsWord distributionsLDA extensions
Rating matrixMeasuring user similarity
User-based collaborative filteringItem-based collaborative filtering
Evaluating binary top-N recommendationsEvaluating non-binary top-N recommendationsEvaluating individual predictions

Content preview from Mastering Predictive Analytics with R

Predicting promoter gene sequences

The first application we will study in detail comes from the field of biology. There, we learn that the basic building blocks of DNA molecules are actually four fundamental molecules known as nucleotides. These are called Thymine, Cytosine, Adenine, and Guanine, and it is the order in which these molecules appear in a DNA strand that encodes the genetic information carried by the DNA.

An interesting problem in molecular biology is finding promoter sequences within a larger DNA strand. These are special sequences of nucleotides that play an important role in regulating a genetic process known as gene transcription. This is the first step in the mechanism by which information in the DNA is read.

The molecular biology ...