Machine Learning with RPackt Publishing Ltd, 25 okt 2013 - 396 pagina's Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required. |
Inhoudsopgave
data | |
IntroducingMachine Learning | |
Finding Groups of Data Clustering with kmeans | |
Understanding clustering | |
Summary | |
Evaluating Model Performance Measuring performance for classification | |
Summary | |
Specialized Machine Learning Topics | |
Overige edities - Alles bekijken
Veelvoorkomende woorden en zinsdelen
activation function additional analysis apply Apriori Apriori algorithm association rules backpropagation boxplot calculate canbe Chapter classifier clusters columns confusion matrix correlation create data frame data structures databases decision trees default dependent variable diagram error estimated evaluating model performance example FALSE following command groups identify improving model performance indicates input install instance inthe itemset kappa statistic kmeans labeled large number Lazy Learning learning task linear regression machine learning machine learning algorithms measures methods mileage model trees mushrooms naive Bayes nearest neighbor neural network neurons nodes numeric prediction ofthe Oring output overfitting package parameter patterns percent probability problem provides quantiles random realworld regression model regression trees relationships rows rule learner scatterplot simple SMS messages spam sparse matrix specify split statistics Step stringsAsFactors summary Support Vector Machines temperature test datasets thatthe thedata training data values Viagra Visualizing