Finding optimal region of a continuous search space
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I'm working on a problem in which I've got large table of data with two features and three target columns and I need to find the optimal filter parameters which theoretically would give me:
1) most preferable target distribution, i.e. highest mean & smallest std on positive target columns and the lowest mean and smallest std on negative target columns
2) largest subset possible
the filtering is done on 2 columns only, so four possible parameters, i.e. min/max of each column and there are three columns which determine the 'character' of the target distribution
since the data is already tabular and continuous, I've just set up a grid search and then I scan for the global max, but as I'm doing this in 2D I can only evaluate on one of the three columns which determine actual character of the target distribution. evaluation of the results that this generates are less than optimal.
I have a sense that there is a much better way to formulate the problem, e.g. with decision trees or a probabilistic model perhaps, but I'm at a loss as to how to go about it. and as I'm new to this, I'm not sure even how to research the issue.
Anybody have direction or suggestions about how to better approach this?
decision-trees optimization probabilistic-programming
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I'm working on a problem in which I've got large table of data with two features and three target columns and I need to find the optimal filter parameters which theoretically would give me:
1) most preferable target distribution, i.e. highest mean & smallest std on positive target columns and the lowest mean and smallest std on negative target columns
2) largest subset possible
the filtering is done on 2 columns only, so four possible parameters, i.e. min/max of each column and there are three columns which determine the 'character' of the target distribution
since the data is already tabular and continuous, I've just set up a grid search and then I scan for the global max, but as I'm doing this in 2D I can only evaluate on one of the three columns which determine actual character of the target distribution. evaluation of the results that this generates are less than optimal.
I have a sense that there is a much better way to formulate the problem, e.g. with decision trees or a probabilistic model perhaps, but I'm at a loss as to how to go about it. and as I'm new to this, I'm not sure even how to research the issue.
Anybody have direction or suggestions about how to better approach this?
decision-trees optimization probabilistic-programming
asked yesterday
JedJed
62
New contributor
Jed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
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I'm working on a problem in which I've got large table of data with two features and three target columns and I need to find the optimal filter parameters which theoretically would give me:
1) most preferable target distribution, i.e. highest mean & smallest std on positive target columns and the lowest mean and smallest std on negative target columns
2) largest subset possible
the filtering is done on 2 columns only, so four possible parameters, i.e. min/max of each column and there are three columns which determine the 'character' of the target distribution
since the data is already tabular and continuous, I've just set up a grid search and then I scan for the global max, but as I'm doing this in 2D I can only evaluate on one of the three columns which determine actual character of the target distribution. evaluation of the results that this generates are less than optimal.
I have a sense that there is a much better way to formulate the problem, e.g. with decision trees or a probabilistic model perhaps, but I'm at a loss as to how to go about it. and as I'm new to this, I'm not sure even how to research the issue.
Anybody have direction or suggestions about how to better approach this?
decision-trees optimization probabilistic-programming
asked yesterday
JedJed
62
New contributor
Jed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm working on a problem in which I've got large table of data with two features and three target columns and I need to find the optimal filter parameters which theoretically would give me:
1) most preferable target distribution, i.e. highest mean & smallest std on positive target columns and the lowest mean and smallest std on negative target columns
2) largest subset possible
the filtering is done on 2 columns only, so four possible parameters, i.e. min/max of each column and there are three columns which determine the 'character' of the target distribution
since the data is already tabular and continuous, I've just set up a grid search and then I scan for the global max, but as I'm doing this in 2D I can only evaluate on one of the three columns which determine actual character of the target distribution. evaluation of the results that this generates are less than optimal.
I have a sense that there is a much better way to formulate the problem, e.g. with decision trees or a probabilistic model perhaps, but I'm at a loss as to how to go about it. and as I'm new to this, I'm not sure even how to research the issue.
Anybody have direction or suggestions about how to better approach this?
decision-trees optimization probabilistic-programming
decision-trees optimization probabilistic-programming
asked yesterday
JedJed
62
New contributor
Jed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
JedJed
62
New contributor
Jed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
JedJed
62
New contributor
Jed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
JedJed
62
asked yesterday
JedJed
62
62
New contributor
Jed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Jed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Jed is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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