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Table 8 Characteristics of the C4.5 decision tree algorithm to analyze the portfolio and classify customers

From: An analytics model for TelecoVAS customers’ basket clustering using ensemble learning approach

Parameter Description
Entrance Training examples
The core of the decision tree CoreGain-Ratio
Output Customer classification
Maximum tree depth 20
Pruning the tree Able to prune the tree
Confidence rate 0.25
Pruning the tree Predict tree pruning
Minimum leaf size 2
Minimum size of tree leaf separation 4
Number of lessons per round 3