What Exactly Do You Mean By Pruning In A Decision Tree?

1.	What Exactly Do You Mean By Pruning In A Decision Tree?
Answer» Many times it has been seen that in a cluster the predictive powers are very weak and they need to be removed. This is to cut down the overall complexity of the model or to increase the accuracy. This condition is generally considered as Pruning. There is a strict limit on pruning otherwise it MAKES the model totally USELESS. The LATEST version available in the MACHINE LEARNING algorithms is Reduced error pruning. Many times it has been seen that in a cluster the predictive powers are very weak and they need to be removed. This is to cut down the overall complexity of the model or to increase the accuracy. This condition is generally considered as Pruning. There is a strict limit on pruning otherwise it makes the model totally useless. The latest version available in the machine learning algorithms is Reduced error pruning.

Answer»

Many times it has been seen that in a cluster the predictive powers are very weak and they need to be removed. This is to cut down the overall complexity of the model or to increase the accuracy. This condition is generally considered as Pruning. There is a strict limit on pruning otherwise it MAKES the model totally USELESS. The LATEST version available in the MACHINE LEARNING algorithms is Reduced error pruning.

Many times it has been seen that in a cluster the predictive powers are very weak and they need to be removed. This is to cut down the overall complexity of the model or to increase the accuracy. This condition is generally considered as Pruning. There is a strict limit on pruning otherwise it makes the model totally useless. The latest version available in the machine learning algorithms is Reduced error pruning.

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