Can Anyone Explain The Guest Subset Ratio And Subset Ratio In Random Forest
Can Anyone Explain The Guest Subset Ratio And Subset Ratio In Random Forest
I don't understand what is the goal of subset ration in random forest in RapidMiner
I don't understand what is the goal of subset ration in random forest in RapidMiner
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Thank you very much
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Hi!
First, it's "guess subset ratio", "guess" as in "try to determine a good value".
The subset ratio in Random Forest is used to select a random subset of attributes in every step when creating the trees to build the forest. The ratio is applied to the number of all attributes. If the ratio is 0.3, for example, and you have 100 attributes, 30 will be selected randomly for each new step.
You could try different values, but if you have enough trees, the effect of this setting will be small, as decision trees are already quite good at attribute selection.
If the subset ratio is too small, you will often have a situation where the tree doesn't have relevant attributes to choose. If it is too high, the trees will be less random and more similar to each other, which could lead to an overfitted model.
Regards,
Balázs
First, it's "guess subset ratio", "guess" as in "try to determine a good value".
The subset ratio in Random Forest is used to select a random subset of attributes in every step when creating the trees to build the forest. The ratio is applied to the number of all attributes. If the ratio is 0.3, for example, and you have 100 attributes, 30 will be selected randomly for each new step.
You could try different values, but if you have enough trees, the effect of this setting will be small, as decision trees are already quite good at attribute selection.
If the subset ratio is too small, you will often have a situation where the tree doesn't have relevant attributes to choose. If it is too high, the trees will be less random and more similar to each other, which could lead to an overfitted model.
Regards,
Balázs
First, it's "guess subset ratio", "guess" as in "try to determine a good value".
The subset ratio in Random Forest is used to select a random subset of attributes in every step when creating the trees to build the forest. The ratio is applied to the number of all attributes. If the ratio is 0.3, for example, and you have 100 attributes, 30 will be selected randomly for each new step.
You could try different values, but if you have enough trees, the effect of this setting will be small, as decision trees are already quite good at attribute selection.
If the subset ratio is too small, you will often have a situation where the tree doesn't have relevant attributes to choose. If it is too high, the trees will be less random and more similar to each other, which could lead to an overfitted model.
Regards,
Balázs