After successful execution, the tool generates the summary and presents the algorithm’s effectiveness based on different parameters: To begin with, this classifier is the implementation of the 0-R classifier and allows batch processing. Let’s apply ZeroR classifier to the dataset. Other than these, we can also use more test options such as Preserve order for % split, Output source code, etc. started Weka, opened Package manager, installed the plugin. downloaded and installed GraphViz 2.38 from Softpedia ( wasnt responding) downloaded the 2014.8.1 release of the plugin.
Install weka on linux windows 7#
Install weka on linux archive#
Supplied test set – evaluates the classifier based on a separate test set I download a zipped archive containing Weka as given on I then unzipped the zip file.Use training set – the classifier will be tested on the same training set.The classic examples of classification are: declaring a brain tumor as “malignant” or “benign” or assigning an email to “spam” or “not_spam” class.Īfter the selection of the desired classifier, we select test options for the training set. These configurations can be editable once the algorithm is selected to use.Ĭlassification is one of the essential functions in machine learning, where we assign classes or categories to items.
Install weka on linux install#
Some of the configuration params are common across all the algorithms, while some are specific. There are two different ways to install Linux on a Chromebook, you can either do it using Gallium OS or ChrUbuntu or in a Chroot environment using Crouton. trees – contains algorithms that use decision trees, such as J48, RandomForestĮach algorithm comes up with configuration params such as batchSize, debug, etc.rules – combines algorithms that use rules such as OneR, ZeroR.misc – miscellaneous algorithms that do not fit any of given category.meta – consists of those algorithms that use or integrate multiple algorithms for their work like Stacking, Bagging.lazy – covers all algorithms that use lazy learning similar to KStar, LWL.functions – comprises the algorithms that estimate a function, including Linear Regression.bayes – consists of algorithms based on Bayes theorem like Naive Bayes.Let’s look at those groups and their core nature: change into the directory and create a Python virtual environment: cd weka-notebooks virtualenv -p /usr/bin/python3.5 venv.
These are available under the “Explorer” tab of the WEKA. The following worked on Linux Mint 18.2: create a directory called weka-notebooks. All the algorithms, because of their core nature, are divided into several groups. WEKA provides ample amounts of algorithms for machine learning tasks.
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