Download Weka for free. Weka is a collection of machine learning algorithms for solving real-world data mining problems. This app is written in Java and runs on almost any platform. Weka mac, Weka for Mac (Waikato Environment for Knowledge Analysis) is a popular suite of machine l. Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. Weka is a data mining application developed on the Java platform that includes a set of algorithms that help you deal with specific tasks. The utility can be used to classify data, to apply association rules, to deal with regression or clustering, or to visualize the results. Makes learning applied machine learning easy, efficient, and fun. It is a GUI tool that allows you to load datasets, run algorithms and design and run experiments with results statistically robust enough to publish. I recommend Weka to beginners in machine learning because it lets them focus on learning the rather than getting bogged down by the and the — those can come later. In this post, I want to show you how easy it is to load a dataset, run an advanced classification algorithm and review the results. If you follow along, you will have machine learning results in under 5 minutes, and the knowledge and confidence to go ahead and try more datasets and more algorithms. Download Weka and Install Visit the and locate a version of Weka suitable for your computer (Windows, Mac, or Linux). Weka requires Java. You may already have installed and if not, there are versions of Weka listed on the download page (for Windows) that include Java and will install it for you. I’m on a Mac myself, and like everything else on Mac, Weka just works out of the box. If you are interested in machine learning, then I know you can figure out how to download and install software into your own computer. If you need help installing Weka, see the following post that provides step-by-step instructions: •. Weka GUI Chooser Click the “ Explorer” button to launch the Weka Explorer. This GUI lets you load datasets and run classification algorithms. It also provides other features, like data filtering, clustering, association rule extraction, and visualization, but we won’t be using these features right now. Open the data/iris.arff Dataset Click the “ Open file” button to open a data set and double click on the “ data” directory. Weka provides a number of small common machine learning datasets that you can use to practice on. Select the “ iris.arff” file to load the Iris dataset. Weka Explorer Interface with the Iris dataset loaded The Iris Flower dataset is a famous dataset from statistics and is heavily borrowed by researchers in machine learning. It contains 150 instances (rows) and 4 attributes (columns) and a class attribute for the species of iris flower (one of setosa, versicolor, and virginica). You can read more about. Select and Run an Algorithm Now that you have loaded a dataset, it’s time to choose a machine learning algorithm to model the problem and make predictions. Click the “ Classify” tab. This is the area for running algorithms against a loaded dataset in Weka. You will note that the “ ZeroR” algorithm is selected by default. Click the “ Start” button to run this algorithm. Weka Results for the ZeroR algorithm on the Iris flower dataset The ZeroR algorithm selects the majority class in the dataset (all three species of iris are equally present in the data, so it picks the first one: setosa) and uses that to make all predictions. This is the baseline for the dataset and the measure by which all algorithms can be compared. The result is 33%, as expected (3 classes, each equally represented, assigning one of the three to each prediction results in 33% classification accuracy). You will also note that the test options selects Cross Validation by default with 10 folds. This means that the dataset is split into 10 parts: the first 9 are used to train the algorithm, and the 10th is used to assess the algorithm. This process is repeated, allowing each of the 10 parts of the split dataset a chance to be the held-out test set. The ZeroR algorithm is important, but boring. Click the “Choose” button in the “Classifier” section and click on “trees” and click on the “J48” algorithm. This is an implementation of the C4.8 algorithm in Java (“J” for Java, 48 for C4.8, hence the J48 name) and is a minor extension to the famous C4.5 algorithm. Click the “ Start” button to run the algorithm. Just the results of the J48 algorithm on the Iris flower dataset in Weka Firstly, note the. You can see that the model achieved a result of 144/150 correct or 96%, which seems a lot better than the baseline of 33%. Secondly, look at the. You can see a table of actual classes compared to predicted classes and you can see that there was 1 error where an Iris-setosa was classified as an Iris-versicolor, 2 cases where Iris-virginica was classified as an Iris-versicolor, and 3 cases where an Iris-versicolor was classified as an Iris-setosa (a total of 6 errors). This table can help to explain the accuracy achieved by the algorithm.
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