Weka - Data mining Tool W eka is a tool for big data and data mining. It is used to various classification, experiments, and analysis over large data sets. Installation Guide -weka You can download Weka from here and follow the normal installation procedure. After completion you will get following window, here you can begin your classification or experiment on different data sets with Weka.
Weka - Data Mining Tool
Algorithms: There are a lot of algorithm in weka for various classification and experiments and some the major and widely used are following :
Decision tree(J48):
NAME:
weka.classifiers.trees.J48
SYNOPSIS:
Class for generating a pruned or Unpruned C4.5 decision tree.
Naïve Bayes:
NAME:
weka.classifiers.bayes.NaiveBayes
SYNOPSIS:
Class for a Naive Bayes classifier using estimator classes. Numeric estimator
precision values are chosen based on analysis of the training data. For this
reason, the classifier is not an UpdateableClassifier (which in typical usage
are initialized with zero training instances)
KNN(IBK):
NAME:
weka.classifiers.lazy.IBk
SYNOPSIS:
K-nearest neighbours classifier. Can select appropriate value of K based on
cross-validation. Can also do distance weighting.
SVM(LibSVM):
NAME:
weka.classifiers.functions.LibSVM
SYNOPSIS: A
wrapper class for the libsvm tools (the libsvm classes, typically the jar file,
need to be in the classpath to use this classifier). LibSVM runs faster than
SMO since it uses LibSVM to build the SVM classifier. LibSVM allows users to
experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM
tool. LibSVM reports many useful statistics about LibSVM classifier (e.g.,
confusion matrix,precision, recall, ROC score, etc.).
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