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The results also show the importance of pruning procedures to identify parsimonious FNN models.
Understand the importance of pruning.
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This is a clear effect of pruning for indirect interactions, since the relative importance of a node is now given in terms of its direct connections and not because of neighbor-by-neighbor influence.
Commercial growers do a lot of pruning.
GARDENERS understand the virtue of pruning.
The performance of pruning model was the same with that of without pruning model.
To avoid this, we propose two methods of pruning.
Set procedures of pruning are followed by using secateurs, knives, pruning saw, etc.
Grafting is the reverse of pruning a leaf.
To improve the basic RF, an ensemble pruning algorithm based on a back propagation neural network that can evaluate the importance of each tree and prune "detrimental" trees is developed.
But a bit of pruning is justified.
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