By Ashish Gupta
Build and customize your personal classifiers utilizing Apache Mahout
About This Book
- Explore the differing kinds of category algorithms on hand in Apache Mahout
- Create and review your individual ready-to-use class types utilizing genuine international datasets
- A sensible advisor to difficulties confronted in class with techniques defined in an easy-to-understand manner
Who This ebook Is For
If you're a facts scientist who has a few event with the Hadoop surroundings and laptop studying equipment and wish to attempt out type on huge datasets utilizing Mahout, this booklet is perfect for you. wisdom of Java is essential.
What you'll Learn
- Apply laptop studying innovations within the quarter of classification
- Categorize the unknown goods by utilizing the class version in Apache Mahout
- Use the classifier to categorise textual content documents
- Implement a multilayer perceptron to map units of enter to acceptable output sets
- Develop the Hidden Markov version for a method with hidden states
- Build and installation an electronic mail classifier which may expect the supply of incoming mail
This e-book is a realistic consultant that explains the category algorithms supplied in Apache Mahout with assistance from genuine examples. beginning with the creation of category and version assessment options, we'll discover Apache Mahout and examine why it's a sensible choice for classification.
Next, you are going to find out about various category algorithms and types similar to the Naive Bayes set of rules, the Hidden Markov version, and so on.
Finally, besides the examples that help you within the production of versions, this booklet lets you construct a mail category procedure that may be produced once it really is built. After studying this e-book, it is possible for you to to appreciate the idea that of class and some of the algorithms besides the artwork of establishing your individual classifiers.
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Additional info for Learning Apache Mahout Classification
More explanatory fields will expand the hypothesis space and will be useful to overcome this problem. Both overfitting and underfitting provide poor results with new datasets. Mahout supports logistic regression trained via Stochastic Gradient Descent. Naïve Bayes classification: This is a very popular algorithm for text classification. We will discuss vectorization, bag of words, n-grams, and other terms used in text classification. Hidden Markov Model (HMM): This is used in various fields, such as speech recognition, parts-of-speech tagging, gene prediction, time-series analysis, and so on.
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Positive predictive value (precision) * sensitivity (recall))/(Positive predictive value (precision) +sensitivity (recall))). The closer the value is to 1, the greater is your classifier. The entropy matrix Before going into the details of the entropy matrix, first we need to understand entropy. The concept of entropy in information theory was developed by Shannon. It is defined as: Entropy = -p1log(p1) – p2log(p2)- ……. Here, we have two properties (classes): eligible or not eligible. A good model will have small negative numbers along the diagonal and will have large negative numbers in the offdiagonal position.
Learning Apache Mahout Classification by Ashish Gupta