BUILDING A DECISION TREES
USING WEKA TOOL
What is Decision tree ?
• Decision Trees are a type of Supervised Machine Learning (that is you
explain what the input is and what the corresponding output is in the
training data) where the data is continuously split according to a
certain parameter.
• The tree can be explained by two entities, namely
- decision nodes and
- Leaves
• The leaves are the decisions or
the final outcomes.
And the decision nodes are where the
data is split.
• Decision trees are a classic supervised learning algorithms, easy to
understand and easy to use.
The main concept behind decision tree learning is the following:
• starting from the training data, we will build a predictive model which
is mapped to a tree structure.
• The goal is to achieve perfect classification with minimal number of
decision, although not always possible due to noise or inconsistencies
in data.
How to download Weka ?
• Weka (Waikato Environment for Knowledge Analysis).
• https://sourceforge.net/projects/weka/
Sample WEKA Datasets available
• https://storm.cis.fordham.edu/~gweiss/data-mining/datasets.html