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 |