Why log-transform to normal distribution for decision trees?
On page 304 of chapter 8 of An Introduction to Statistical Learning with Applications in R (James et al.), the authors say:
We use the Hitters data set to predict a baseball player’s Salary based on Years (the number of years that he has played in the major leagues) and Hits (the number of hits that he made in the previous year). We first remove observations that are missing Salary values, and log-transform Salary so that its distribution has more of a typical bell-shape. (Recall that Salary is measured in thousands of dollars.)
No additional motivation for the log-transform is given. Being that the data are being fed into decision tree algorithms, why was it important to force the data into a normal distribution? I thought most/all decision tree algorithms were invariant to scale changes.
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On page 304 of chapter 8 of An Introduction to Statistical Learning with Applications in R (James et al.), the authors say:
We use the Hitters data set to predict a baseball player’s Salary based on Years (the number of years that he has played in the major leagues) and Hits (the number of hits that he made in the previous year). We first remove observations that are missing Salary values, and log-transform Salary so that its distribution has more of a typical bell-shape. (Recall that Salary is measured in thousands of dollars.)
No additional motivation for the log-transform is given. Being that the data are being fed into decision tree algorithms, why was it important to force the data into a normal distribution? I thought most/all decision tree algorithms were invariant to scale changes.
machine-learning cart
edited 1 hour ago
Sycorax
38.9k1197195
asked 2 hours ago
jss367
1084
New contributor
jss367 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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On page 304 of chapter 8 of An Introduction to Statistical Learning with Applications in R (James et al.), the authors say:
We use the Hitters data set to predict a baseball player’s Salary based on Years (the number of years that he has played in the major leagues) and Hits (the number of hits that he made in the previous year). We first remove observations that are missing Salary values, and log-transform Salary so that its distribution has more of a typical bell-shape. (Recall that Salary is measured in thousands of dollars.)
No additional motivation for the log-transform is given. Being that the data are being fed into decision tree algorithms, why was it important to force the data into a normal distribution? I thought most/all decision tree algorithms were invariant to scale changes.
machine-learning cart
edited 1 hour ago
Sycorax
38.9k1197195
asked 2 hours ago
jss367
1084
New contributor
jss367 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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On page 304 of chapter 8 of An Introduction to Statistical Learning with Applications in R (James et al.), the authors say:
We use the Hitters data set to predict a baseball player’s Salary based on Years (the number of years that he has played in the major leagues) and Hits (the number of hits that he made in the previous year). We first remove observations that are missing Salary values, and log-transform Salary so that its distribution has more of a typical bell-shape. (Recall that Salary is measured in thousands of dollars.)
No additional motivation for the log-transform is given. Being that the data are being fed into decision tree algorithms, why was it important to force the data into a normal distribution? I thought most/all decision tree algorithms were invariant to scale changes.
machine-learning cart
machine-learning cart
edited 1 hour ago
Sycorax
38.9k1197195
asked 2 hours ago
jss367
1084
New contributor
jss367 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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edited 1 hour ago
Sycorax
38.9k1197195
asked 2 hours ago
jss367
1084
New contributor
jss367 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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edited 1 hour ago
Sycorax
38.9k1197195
edited 1 hour ago
Sycorax
38.9k1197195
edited 1 hour ago
Sycorax
38.9k1197195
38.9k1197195
asked 2 hours ago
jss367
1084
New contributor
jss367 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
jss367
1084
asked 2 hours ago
jss367
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1084
New contributor
jss367 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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New contributor
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In this case, the salary is the target of the decision tree, not one of the features (independent variables/predictors). You are correct that decision trees are insensitive to the scale of the predictors, but since I suspect there are a small number of extremely large salaries, transforming the salaries might improve predictions because loss functions which minimize square error will not be so strongly influenced by these large values.
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1 Answer
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In this case, the salary is the target of the decision tree, not one of the features (independent variables/predictors). You are correct that decision trees are insensitive to the scale of the predictors, but since I suspect there are a small number of extremely large salaries, transforming the salaries might improve predictions because loss functions which minimize square error will not be so strongly influenced by these large values.
answered 1 hour ago
Sycorax
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In this case, the salary is the target of the decision tree, not one of the features (independent variables/predictors). You are correct that decision trees are insensitive to the scale of the predictors, but since I suspect there are a small number of extremely large salaries, transforming the salaries might improve predictions because loss functions which minimize square error will not be so strongly influenced by these large values.
answered 1 hour ago
Sycorax
38.9k1197195
add a comment |
In this case, the salary is the target of the decision tree, not one of the features (independent variables/predictors). You are correct that decision trees are insensitive to the scale of the predictors, but since I suspect there are a small number of extremely large salaries, transforming the salaries might improve predictions because loss functions which minimize square error will not be so strongly influenced by these large values.
answered 1 hour ago
Sycorax
38.9k1197195
In this case, the salary is the target of the decision tree, not one of the features (independent variables/predictors). You are correct that decision trees are insensitive to the scale of the predictors, but since I suspect there are a small number of extremely large salaries, transforming the salaries might improve predictions because loss functions which minimize square error will not be so strongly influenced by these large values.
answered 1 hour ago
Sycorax
38.9k1197195
answered 1 hour ago
Sycorax
38.9k1197195
answered 1 hour ago
Sycorax
38.9k1197195
answered 1 hour ago
Sycorax
38.9k1197195
38.9k1197195
add a comment |
add a comment |
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