I am trying to do some kind of parameter values optimization loop with Random Forest, which is going to vary different set of parameter values.

I am going to explain it on base of the iris dataset:

Goal --> I need to get as many species of setosa as possible

The way --> i must find the best (representative) combination of: "Sepal.Length","Sepal.Width","Petal.Length","Petal.Width" values, which identify only setosa and not any other species.

I would appreciate any help as i cannot find any hint or way how to handle this problem.

--> mean of the parameters set in the group of setosa is not sufficient! (as in my original dataset the difference between groups are not that clear!)

To get a bit background of my real problem is that i have different temperature sensors at different points in the production line, and i must identify which combination of those temperatures leads to good quality product, and which combination leads to bad quality product, i think it is very simple to understand. We must find best combination of temperatures and try to achieve it at the production.

[UPDATE]

I have found a similar question on cross validated (but my target value is a factor, not numeric as in this case, but the main princip is the same:

How would I be able to find for which values of inputs do i get the setosa target variable

), however it has not been answered.

For which set of values of predictors (numerical) will my response variable (factor) be guaranteed to equal a certain level (using Random Forest)

I am going to explain it on base of the iris dataset:

Goal --> I need to get as many species of setosa as possible

The way --> i must find the best (representative) combination of: "Sepal.Length","Sepal.Width","Petal.Length","Petal.Width" values, which identify only setosa and not any other species.

I would appreciate any help as i cannot find any hint or way how to handle this problem.

--> mean of the parameters set in the group of setosa is not sufficient! (as in my original dataset the difference between groups are not that clear!)

To get a bit background of my real problem is that i have different temperature sensors at different points in the production line, and i must identify which combination of those temperatures leads to good quality product, and which combination leads to bad quality product, i think it is very simple to understand. We must find best combination of temperatures and try to achieve it at the production.

[UPDATE]

I have found a similar question on cross validated (but my target value is a factor, not numeric as in this case, but the main princip is the same:

How would I be able to find for which values of inputs do i get the setosa target variable

), however it has not been answered.

I am trying to do some kind of parameter values optimization loop with Random Forest, which is going to vary different set of parameter values.

I am going to explain it on base of the iris dataset:

Goal --> I need to get as many species of setosa as possible

The way --> i must find the best (representative) combination of: "Sepal.Length","Sepal.Width","Petal.Length","Petal.Width" values, which identify only setosa and not any other species.

I would appreciate any help as i cannot find any hint or way how to handle this problem.

--> mean of the parameters set in the group of setosa is not sufficient! (as in my original dataset the difference between groups are not that clear).

[UPDATE]

I have found a similar question on cross validated (but my target value is a factor, not numeric as in this case, but the main princip is the same:

How would I be able to find for which values of inputs do i get the setosa target variable

), however it has not been answered.

I am trying to do some kind of parameter values optimization loop with Random Forest, which is going to vary different set of parameter values.

I am going to explain it on base of the iris dataset:

Goal --> I need to get as many species of setosa as possible

The way --> i must find the best (representative) combination of: "Sepal.Length","Sepal.Width","Petal.Length","Petal.Width" values, which identify only setosa and not any other species.

I would appreciate any help as i cannot find any hint or way how to handle this problem.

--> mean of the parameters set in the group of setosa is not sufficient! (as in my original dataset the difference between groups are not that clear!)

To get a bit background of my real problem is that i have different temperature sensors at different points in the production line, and i must identify which combination of those temperatures leads to good quality product, and which combination leads to bad quality product, i think it is very simple to understand. We must find best combination of temperatures and try to achieve it at the production.

[UPDATE]

I have found a similar question on cross validated (but my target value is a factor, not numeric as in this case, but the main princip is the same:

How would I be able to find for which values of inputs do i get the setosa target variable

), however it has not been answered.

I am trying to do some kind of parameter values optimization loop with Random Forest, which is going to vary different set of parameter values.

I am going to explain it on base of the iris dataset:

Goal --> I need to get as many species of setosa as possible

The way --> i must find the best (representative) combination of: "Sepal.Length","Sepal.Width","Petal.Length","Petal.Width" values, which most probably are going to result in species setosa and not any other species.

I would appreciate any help as i cannot find any hint or way how to handle this problem.

--> mean of the parameters set in the group of setosa is not sufficient! (as in my original dataset the difference between groups are not that clear).

[UPDATE]

I have found a similar question on cross validated (but my target value is a factor, not numeric as in this case), however it has not been answered.

I am trying to do some kind of parameter values optimization loop with Random Forest, which is going to vary different set of parameter values.

I am going to explain it on base of the iris dataset:

Goal --> I need to get as many species of setosa as possible

The way --> i must find the best (representative) combination of: "Sepal.Length","Sepal.Width","Petal.Length","Petal.Width" values, which identify only setosa and not any other species.

I would appreciate any help as i cannot find any hint or way how to handle this problem.

--> mean of the parameters set in the group of setosa is not sufficient! (as in my original dataset the difference between groups are not that clear).

[UPDATE]

I have found a similar question on cross validated (but my target value is a factor, not numeric as in this case, but the main princip is the same:

How would I be able to find for which values of inputs do i get the setosa target variable

), however it has not been answered.