What is J48 in WEKA?

J48 Classifier. It is an algorithm to generate a decision tree that is generated by C4. 5 (an extension of ID3). It is also known as a statistical classifier.

How does J48 algorithm work?

J48 is based on a top-down strategy, a recursive divide and conquer strategy. You select which attribute to split on at the root node, and then you create a branch for each possible attribute value, and that splits the instances into subsets, one for each branch that extends from the root node.

What are clustered instances in WEKA?

A clustering algorithm finds groups of similar instances in the entire dataset. WEKA supports several clustering algorithms such as EM, FilteredClusterer, HierarchicalClusterer, SimpleKMeans and so on. You should understand these algorithms completely to fully exploit the WEKA capabilities.

How do we evaluate clusters in WEKA?

1 Answer

  1. Use training set (default).
  2. In Supplied test set or Percentage split Weka can evaluate clusterings on separate test data if the cluster representation is probabilistic (e.g. for EM).
  3. Classes to clusters evaluation .

What does J48 mean?

J48 is a machine learning decision tree classification algorithm based on Iterative Dichotomiser 3. It is very helpful in examine the data categorically and continuously.

What is the difference between J48 and C4 5?

5 algorithms or can be called as optimized implementation of the C4. 5. The output of J48 is the Decision tree.

What is J48 good for?

J48 can help not only to make accurate predictions from the data but also to explain the patterns in it. It deals with the problems of the numeric attributes, missing values, pruning, estimating error rates, complexity of decision tree induction, and generating rules from trees (Witten and Frank, 1999).

What is the difference between J48 and decision tree?

Decision trees are more likely to face problem of Data over-fitting , In your case ID3 algorithm is facing the issue of data over-fitting. This is the problem of Decision trees ,that it splits the data until it make pure sets. This Problem of Data over-fitting is fixed in it’s extension that is J48 by using Pruning.

What is K-means clustering explain with an example?

K-means clustering algorithm computes the centroids and iterates until we it finds optimal centroid. It assumes that the number of clusters are already known. It is also called flat clustering algorithm. The number of clusters identified from data by algorithm is represented by ‘K’ in K-means.

How do we evaluate clusters in Weka?