By Ashish Gupta
Build and customize your individual classifiers utilizing Apache Mahout
About This Book
- Explore the differing kinds of type algorithms to be had in Apache Mahout
- Create and review your individual ready-to-use type types utilizing genuine international datasets
- A useful consultant to difficulties confronted in category with ideas defined in an easy-to-understand manner
Who This booklet Is For
If you're a facts scientist who has a few adventure with the Hadoop surroundings and laptop studying equipment and need to aim out class on huge datasets utilizing Mahout, this e-book is perfect for you. wisdom of Java is essential.
What you'll Learn
- Apply computing device studying suggestions within the zone of classification
- Categorize the unknown goods through the use of the category version in Apache Mahout
- Use the classifier to categorise textual content documents
- Implement a multilayer perceptron to map units of enter to suitable output sets
- Develop the Hidden Markov version for a procedure with hidden states
- Build and set up an electronic mail classifier which may expect the supply of incoming mail
In Detail
This ebook is a realistic advisor that explains the class algorithms supplied in Apache Mahout with the aid of real examples. beginning with the creation of type and version evaluate concepts, we'll discover Apache Mahout and examine why it's a good selection for classification.
Next, you are going to find out about assorted class algorithms and versions equivalent to the Naive Bayes set of rules, the Hidden Markov version, and so on.
Finally, besides the examples that help you within the construction of types, this e-book enables you to construct a mail type method that may be produced once it's built. After interpreting this publication, it is possible for you to to appreciate the idea that of category and some of the algorithms besides the artwork of establishing your personal classifiers.
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Additional resources for Learning Apache Mahout Classification
Example text
The table of confusion has two rows and two columns that report about true positive, true negative, false positive, and false negative. Therefore, accuracy = (a+d)/(a+b+c+d). Therefore, precision = a/(a+b). Therefore, negative predictive value = d/(c+d). Therefore, sensitivity = a/(a+c). Therefore, specificity =d /(b+d). ((Positive predictive value (precision) * sensitivity (recall))/(Positive predictive value (precision) +sensitivity (recall))). The closer the value is to 1, the greater is your classifier.
Mahout provides full API support to develop your own user-based or item-based recommendation engine. For example, Mahout can be used to build a document classifier or an e-mail classifier. The main difference between clustering and classification is that in classification, we know the end class name. For clustering, Mahout has already implemented some of the most popular algorithms in this area, such as k-means, fuzzy k-means, canopy, and so on. Singular value decomposition and Lanczos are examples of the algorithms that Mahout provides.
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