## Machine Learning with RPackt Publishing Ltd, 25 Eki 2013 - 396 sayfa 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. |

### İçindekiler

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### Diğer baskılar - Tümünü görüntüle

### Sık kullanılan terimler ve kelime öbekleri

accuracy apply Apriori Apriori algorithm association rules boosting C5.0 decision tree calculate Chapter classifier clusters columns confusion matrix correlation create cross-validation data frame Data preparation data structures databases default ensemble error rate estimate evaluating model performance example exploring and preparing FALSE Hadoop holdout identify improving model performance indicates input install instance itemsets JSON k-means k-means algorithm kappa statistic kNN algorithm large number learning task linear regression machine learning machine learning algorithms MapReduce mean median methods model trees mushroom naive Bayes neural networks nodes output parameter percent preparing the data probability problem provides random forests regression model regression trees relationships resampling ROC curve rows rule learners scatterplot SMS messages spam sparse matrix specify split Step summary Support Vector Machines test data test datasets training a model training and test training data training dataset tuning Understanding Visualizing