random forest regression output calculation
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Hi this is a purely theoretical question which i cant get my head around ( and could be completely wrong)
With random forest regressions - you grow n number of trees, each tree uses a subset of the data and in some cases a subset of the available variables to predict the dependent variable. the average of these n number of trees is taken to give us a predicted value. however, is there any need to look at the distribution of predictions at the individual tree level? are we able to obtain a number that provides some certainty of the overall predicted value? i would assume that a more consistent number being produced at the individual tree level would be preferred than a wide variety of numbers?
Thanks in advance
r random-forest
asked Nov 17 '18 at 2:55
ManyQsFewAsManyQsFewAs
163
add a comment |
Hi this is a purely theoretical question which i cant get my head around ( and could be completely wrong)
With random forest regressions - you grow n number of trees, each tree uses a subset of the data and in some cases a subset of the available variables to predict the dependent variable. the average of these n number of trees is taken to give us a predicted value. however, is there any need to look at the distribution of predictions at the individual tree level? are we able to obtain a number that provides some certainty of the overall predicted value? i would assume that a more consistent number being produced at the individual tree level would be preferred than a wide variety of numbers?
Thanks in advance
r random-forest
asked Nov 17 '18 at 2:55
ManyQsFewAsManyQsFewAs
163
add a comment |
Hi this is a purely theoretical question which i cant get my head around ( and could be completely wrong)
With random forest regressions - you grow n number of trees, each tree uses a subset of the data and in some cases a subset of the available variables to predict the dependent variable. the average of these n number of trees is taken to give us a predicted value. however, is there any need to look at the distribution of predictions at the individual tree level? are we able to obtain a number that provides some certainty of the overall predicted value? i would assume that a more consistent number being produced at the individual tree level would be preferred than a wide variety of numbers?
Thanks in advance
r random-forest
asked Nov 17 '18 at 2:55
ManyQsFewAsManyQsFewAs
163
Hi this is a purely theoretical question which i cant get my head around ( and could be completely wrong)
With random forest regressions - you grow n number of trees, each tree uses a subset of the data and in some cases a subset of the available variables to predict the dependent variable. the average of these n number of trees is taken to give us a predicted value. however, is there any need to look at the distribution of predictions at the individual tree level? are we able to obtain a number that provides some certainty of the overall predicted value? i would assume that a more consistent number being produced at the individual tree level would be preferred than a wide variety of numbers?
Thanks in advance
r random-forest
r random-forest
asked Nov 17 '18 at 2:55
ManyQsFewAsManyQsFewAs
163
asked Nov 17 '18 at 2:55
ManyQsFewAsManyQsFewAs
163
edited Nov 17 '18 at 9:38
ManyQsFewAs
asked Nov 17 '18 at 2:55
ManyQsFewAsManyQsFewAs
163
asked Nov 17 '18 at 2:55
ManyQsFewAsManyQsFewAs
163
asked Nov 17 '18 at 2:55
ManyQsFewAsManyQsFewAs
163
163
add a comment |
add a comment |
1 Answer
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This method of determining variable importance has some drawbacks. For data including categorical variables with different number of levels, random forests are biased in favor of those attributes with more levels. Methods such as partial permutations and growing unbiased trees can be used to solve the problem. If the data contain groups of correlated features of similar relevance for the output, then smaller groups are favored over larger groups.
answered Nov 17 '18 at 14:23
Sinchit RajputSinchit Rajput
113
Thanks for your response. I'm more looking at the distribution of out comes for each individual tree within the forest. Is there any value/patteren I should be looking at with each trees prediction?
– ManyQsFewAs
Nov 17 '18 at 19:27
add a comment |
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1 Answer
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1 Answer
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active
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oldest
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oldest
votes
This method of determining variable importance has some drawbacks. For data including categorical variables with different number of levels, random forests are biased in favor of those attributes with more levels. Methods such as partial permutations and growing unbiased trees can be used to solve the problem. If the data contain groups of correlated features of similar relevance for the output, then smaller groups are favored over larger groups.
answered Nov 17 '18 at 14:23
Sinchit RajputSinchit Rajput
113
Thanks for your response. I'm more looking at the distribution of out comes for each individual tree within the forest. Is there any value/patteren I should be looking at with each trees prediction?
– ManyQsFewAs
Nov 17 '18 at 19:27
add a comment |
This method of determining variable importance has some drawbacks. For data including categorical variables with different number of levels, random forests are biased in favor of those attributes with more levels. Methods such as partial permutations and growing unbiased trees can be used to solve the problem. If the data contain groups of correlated features of similar relevance for the output, then smaller groups are favored over larger groups.
answered Nov 17 '18 at 14:23
Sinchit RajputSinchit Rajput
113
Thanks for your response. I'm more looking at the distribution of out comes for each individual tree within the forest. Is there any value/patteren I should be looking at with each trees prediction?
– ManyQsFewAs
Nov 17 '18 at 19:27
add a comment |
This method of determining variable importance has some drawbacks. For data including categorical variables with different number of levels, random forests are biased in favor of those attributes with more levels. Methods such as partial permutations and growing unbiased trees can be used to solve the problem. If the data contain groups of correlated features of similar relevance for the output, then smaller groups are favored over larger groups.
answered Nov 17 '18 at 14:23
Sinchit RajputSinchit Rajput
113
This method of determining variable importance has some drawbacks. For data including categorical variables with different number of levels, random forests are biased in favor of those attributes with more levels. Methods such as partial permutations and growing unbiased trees can be used to solve the problem. If the data contain groups of correlated features of similar relevance for the output, then smaller groups are favored over larger groups.
answered Nov 17 '18 at 14:23
Sinchit RajputSinchit Rajput
113
answered Nov 17 '18 at 14:23
Sinchit RajputSinchit Rajput
113
answered Nov 17 '18 at 14:23
Sinchit RajputSinchit Rajput
113
answered Nov 17 '18 at 14:23
Sinchit RajputSinchit Rajput
113
113
Thanks for your response. I'm more looking at the distribution of out comes for each individual tree within the forest. Is there any value/patteren I should be looking at with each trees prediction?
– ManyQsFewAs
Nov 17 '18 at 19:27
add a comment |
Thanks for your response. I'm more looking at the distribution of out comes for each individual tree within the forest. Is there any value/patteren I should be looking at with each trees prediction?
– ManyQsFewAs
Nov 17 '18 at 19:27
Thanks for your response. I'm more looking at the distribution of out comes for each individual tree within the forest. Is there any value/patteren I should be looking at with each trees prediction?
– ManyQsFewAs
Nov 17 '18 at 19:27
Thanks for your response. I'm more looking at the distribution of out comes for each individual tree within the forest. Is there any value/patteren I should be looking at with each trees prediction?
– ManyQsFewAs
Nov 17 '18 at 19:27
add a comment |
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