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Next youll see how to execute Scala and Python based programs for Spark MLThis site is like a library, you could find million book here by using search box in the widgetSpark clusters 3Building a Classification Model with Spark Types of classification models Linear models Logistic regression Multinomial logistic regression Visualizing the StumbleUpon dataset Extracting features from the Kaggle/StumbleUpon evergreen classification dataset StumbleUponExecutor Linear support vector machines The naive Bayes model Decision trees Ensembles of trees Random Forests Gradient-Boosted Trees Multilayer perceptron classifier Extracting the right features from your data Training classification models Training a classification model on the Kaggle/StumbleUpon evergreen classification dataset Using classification models Generating predictions for the Kaggle/StumbleUpon evergreen classification dataset Evaluating the performance of classification models Accuracy and prediction error Precision and recall ROC curve and AUC Improving model performance and tuning parameters Feature standardization Additional features Using the correct form of data Tuning model parameters Linear models Iterations Step size Regularization Decision trees Tuning tree depth and impurity The naive Bayes model Cross-validation Summary This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionalityWe will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache SparkSummary 11
Chapter 1: Getting Up and Running with Spark Installing and setting up Spark locally Spark clusters The Spark programming model SchemaRDD Spark data frame The first step to a Spark program in Scala The first step to a Spark program in Java The first step to a Spark program in Python The first step to a Spark program in R Getting Spark running on Amazon EC2 Configuring and running Spark on Amazon Elastic Map Reduce UI in Spark Supported machine learning algorithms by Spark Benefits of using Spark ML as compared to existing libraries Spark Cluster on Google Compute Engine - DataProc Summary Style and approach This is a hands-on guide to data analysis and data processingIt walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical modelsAt its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics.If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutionsToward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform
For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the resultsReaders then discover the most fundamental concepts and abstractions of Spark, particularly Resilient Distributed Datasets (RDDs) and the basic data transformations that RDDs provideImproving model performance and tuning parameters 6Hadoop Real World Solutions Cookbook gives readers insights into learning and mastering big data via recipesReasonable knowledge of Scala is expectedfile hosting chart idea box FAQ about blog privacy dmca logos contacts privacy dmca It will help developers who have had problems that were too much to be dealt with on a single computerThis practical guide that covers all of the need to know topics in a very systematic wayWhat's so special about text data? 2In either case, this book will get you up and running quickly 07f867cfac