Normal single-instance prediction is still single-threaded. Now, Bagging, RandomCommittee, and RandomSubSpace implement multi-threaded batch prediction if base learner supports batch prediction (e.g., MLRClassifier). Now, Bagging, RandomCommittee, and RandomSubSpace implement efficient batch prediction if the base learner supports it (e.g., MLRClassifier). ![]() M /trunk/weka/src/main/java/weka/classifiers/meta/RandomSubSpace.java M /trunk/weka/src/main/java/weka/classifiers/meta/RandomCommittee.java M /trunk/weka/src/main/java/weka/classifiers/meta/Bagging.java Now, MultiFilter also checks that at least one of the specified filters can handle instance or attribute weights if the data has unequal attribute or instance weights respectively. If no filter is Randomizable, there will be no effect. The seed that is specified is passed through the filters included in the list specified by the user. M /trunk/weka/src/test/resources/wekarefs/weka/filters/MultiFilterTest.ref M /trunk/weka/src/main/java/weka/gui/AttributeSummaryPanel.javaĭate attributes now display dates instead of milliseconds using the attribute's format for min/max/mean/stdev Removed main method (only used for testing) to clean up dependencies M /trunk/weka/src/main/java/weka/classifiers/evaluation/ThresholdCurve.java M /trunk/weka/src/main/java/weka/classifiers/evaluation/MarginCurve.java M /trunk/weka/src/main/java/weka/classifiers/evaluation/CostCurve.java SubspaceCluster now runs at the command-line even when the user does not explicitly provide a cluster definition, to be consistent with the setup in the GUI. M /trunk/weka/src/main/java/weka/datagenerators/clusterers/SubspaceCluster.java M /trunk/weka/src/main/java/weka/datagenerators/clusterers/SubspaceClusterDefinition.javaįixed a bug where if no -A, -G or -U was specified in the option string, then the default attribute index range would be overwritten with empty string It makes it possible to train any Weka classifier in Spark, for example.R15743 | mhall | 10:42:26 +1300 (Wed, ) | 1 line Conversely, Python toolkits such as scikit-learn can be used from Weka.įor running Weka-based algorithms on truly large datasets, the distributed Weka for Spark package is available. ![]() ![]() Weka's functionality can be accessed from Python using the Python Weka Wrapper. ![]() Weka models can be used, built, and evaluated in R by using the RWeka package for R conversely, R algorithms and visualization tools can be invoked from Weka using the RPlugin package for Weka. WEKA can be integrated with the most popular data science tools. Deep neural networks, including convolutional networks and recurrent networks, can be trained directly from Weka's graphical user interfaces, providing state-of-the-art methods for tasks such as image and text classification. WekaDeeplearning4j is a deep learning package for Weka. Note that programmers can also easily implement this pipeline using Weka's Java API: Second, we select a learning algorithm to use, e.g., the J48 classifier, which learns decision trees.įinally, we run a 10-fold cross-validation evaluation and obtain an estimate of predictive performance. Weka can be used to build machine learning pipelines, train classifiers, and run evaluations without having to write a single line of code:įirst, we open the dataset that we would like to evaluate. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |