Keep code clean

If you haven’t read the previous parts of this article: Part 1 Part 2 Part 3 Required dependencies To start using Weka, the following dependency has to be imported: <dependency> <groupId>nz.ac.waikato.cms.weka</groupId> <artifactId>weka-stable</artifactId> <version>3.6.14</version> </dependency> Classifier class In the previous part, we’ve already created  a class for creating .arff  configuration. So, it’s time to create a classifier that processes that configuration. So, we’ve got a wrapper for Weka classifier that allows us to classify instance and evaluate classifier. Details of how…

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If you haven’t read the previous parts of this article: Part 1 Part 2 As we remember, .arff configuration example presented in the previous part of the article had @DATA section with a list of feature samples. Let’s create a class that contains feature values and an optional class value. Class value is optional because when we perform classification we have feature values, but don’t have class value, it is going to be determined by classifier. Let’s create a class…

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Iris classification problem has been described in a previous article. If you haven’t read it, then welcome to Part 1. In order to perform classification we have to teach our classifier. The more training data we have, the more accurate results we’ll get. I’ve taken learning data from “Machine learning repository”. It has a lot of diverse data sets for you to play with. Iris data set contains the following information: Sepal length in cm Sepal width in cm Petal…

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Amazing flowers, aren’t they ? Let’s imagine us walking across a wildlife area and observing beautiful flowers we’ve never seen before. We like them so much and want to see them again but all we know is only that they look like Iris. Checking contents of our backpack we realize that we’re lucky to have a ruler with us. So, we can measure some characteristics: sepal length sepal width petal length petal width We’re getting closer to classification problem. The…

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