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How do you search a high dimensional for the global maxima using as few samples as possible?
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsHow to decide the number of trees parameter for Random Forest algorithm in PySpark MLlib?Is removing poorly predicted data points a valid approach?How to optimize for time correlated hidden function - the magical candy machineIs it possible to have a validation error less than train error for a while followed by the reverse behaviour?Is there a definitive and more conclusive way of interpreting the R^2 score from a linear regression model in terms of prediction accuracy?What should be the requirement for training data in order to obtain a good regression model using neural network?Voting classifier using grid search for Time SeriesHow can I avoid requiring global information for performing regression on meter variables?How can someone avoid over fitting or data leak in ridge and lasso regression when the training score is high and test score is low?Understanding output of LSTM for regression
$begingroup$
Suppose the value at any point in the space is defined by Y = f(x1, x2 .. xk). For simplicity, we can assume that x takes only binary values. Which means that we have a total of 2^k possible values. I want to find the point in this k-dimensional space such that Y at this point is the highest.
The constraint lies in the cost of calculating Y. Because it is very 'expensive' to calculate Y, I want to do it as infrequently as possible. That being said, I have the ability to measure Y for any point in this k-dimensional space. I'm looking for a way to use only a small subset of values n, where n << 2^k and use it build a prediction model that extrapolates to the entire space
I've been able to do this by starting off with a set of randomly selected n points and using them to construct a regression tree. The prediction error is acceptable. But the size of n is still quite high.
Is there a more intelligent way to choose my n samples? What are some alternative approaches to extrapolate n measurements into the entire feature space?
If I'm willing to settle for the local maxima, how can I find it? Seems like a straight forward problem in linear algebra. But it's been ages and I could really use some help. TIA!
regression grid-search
asked Mar 7 at 5:07
user69058user69058
1
$endgroup$
add a comment |
$begingroup$
Suppose the value at any point in the space is defined by Y = f(x1, x2 .. xk). For simplicity, we can assume that x takes only binary values. Which means that we have a total of 2^k possible values. I want to find the point in this k-dimensional space such that Y at this point is the highest.
The constraint lies in the cost of calculating Y. Because it is very 'expensive' to calculate Y, I want to do it as infrequently as possible. That being said, I have the ability to measure Y for any point in this k-dimensional space. I'm looking for a way to use only a small subset of values n, where n << 2^k and use it build a prediction model that extrapolates to the entire space
I've been able to do this by starting off with a set of randomly selected n points and using them to construct a regression tree. The prediction error is acceptable. But the size of n is still quite high.
Is there a more intelligent way to choose my n samples? What are some alternative approaches to extrapolate n measurements into the entire feature space?
If I'm willing to settle for the local maxima, how can I find it? Seems like a straight forward problem in linear algebra. But it's been ages and I could really use some help. TIA!
regression grid-search
asked Mar 7 at 5:07
user69058user69058
1
$endgroup$
add a comment |
$begingroup$
Suppose the value at any point in the space is defined by Y = f(x1, x2 .. xk). For simplicity, we can assume that x takes only binary values. Which means that we have a total of 2^k possible values. I want to find the point in this k-dimensional space such that Y at this point is the highest.
The constraint lies in the cost of calculating Y. Because it is very 'expensive' to calculate Y, I want to do it as infrequently as possible. That being said, I have the ability to measure Y for any point in this k-dimensional space. I'm looking for a way to use only a small subset of values n, where n << 2^k and use it build a prediction model that extrapolates to the entire space
I've been able to do this by starting off with a set of randomly selected n points and using them to construct a regression tree. The prediction error is acceptable. But the size of n is still quite high.
Is there a more intelligent way to choose my n samples? What are some alternative approaches to extrapolate n measurements into the entire feature space?
If I'm willing to settle for the local maxima, how can I find it? Seems like a straight forward problem in linear algebra. But it's been ages and I could really use some help. TIA!
regression grid-search
asked Mar 7 at 5:07
user69058user69058
1
$endgroup$
Suppose the value at any point in the space is defined by Y = f(x1, x2 .. xk). For simplicity, we can assume that x takes only binary values. Which means that we have a total of 2^k possible values. I want to find the point in this k-dimensional space such that Y at this point is the highest.
The constraint lies in the cost of calculating Y. Because it is very 'expensive' to calculate Y, I want to do it as infrequently as possible. That being said, I have the ability to measure Y for any point in this k-dimensional space. I'm looking for a way to use only a small subset of values n, where n << 2^k and use it build a prediction model that extrapolates to the entire space
I've been able to do this by starting off with a set of randomly selected n points and using them to construct a regression tree. The prediction error is acceptable. But the size of n is still quite high.
Is there a more intelligent way to choose my n samples? What are some alternative approaches to extrapolate n measurements into the entire feature space?
If I'm willing to settle for the local maxima, how can I find it? Seems like a straight forward problem in linear algebra. But it's been ages and I could really use some help. TIA!
regression grid-search
regression grid-search
asked Mar 7 at 5:07
user69058user69058
1
asked Mar 7 at 5:07
user69058user69058
1
asked Mar 7 at 5:07
user69058user69058
1
asked Mar 7 at 5:07
user69058user69058
1
asked Mar 7 at 5:07
user69058user69058
1
1
add a comment |
add a comment |
1 Answer
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$begingroup$
I think you probably should use some randomized optimization algorithm such as randomized hill climbing or genetic algorithm. These are probably more suited for such a problem
answered Mar 7 at 18:12
Wassim9429Wassim9429
171
$endgroup$
add a comment |
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$begingroup$
I think you probably should use some randomized optimization algorithm such as randomized hill climbing or genetic algorithm. These are probably more suited for such a problem
answered Mar 7 at 18:12
Wassim9429Wassim9429
171
$endgroup$
add a comment |
$begingroup$
I think you probably should use some randomized optimization algorithm such as randomized hill climbing or genetic algorithm. These are probably more suited for such a problem
answered Mar 7 at 18:12
Wassim9429Wassim9429
171
$endgroup$
add a comment |
$begingroup$
I think you probably should use some randomized optimization algorithm such as randomized hill climbing or genetic algorithm. These are probably more suited for such a problem
answered Mar 7 at 18:12
Wassim9429Wassim9429
171
$endgroup$
I think you probably should use some randomized optimization algorithm such as randomized hill climbing or genetic algorithm. These are probably more suited for such a problem
answered Mar 7 at 18:12
Wassim9429Wassim9429
171
answered Mar 7 at 18:12
Wassim9429Wassim9429
171
answered Mar 7 at 18:12
Wassim9429Wassim9429
171
answered Mar 7 at 18:12
Wassim9429Wassim9429
171
171
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