1. A computer-implemented ranking-based method for evaluating regression models by obtaining predictions y from one or more models to be evaluated on a test set of customers for which the true value y of the quantity of interest has been observed comprising the steps of:
storing the test set in an electronic database sorted in increasing order such that \u201clarge\u201d corresponds to one of a plurality of customers andor potential customers with largest perceived spending budget, and \u201csmall\u201d corresponds to one of a plurality of customers andor potential customers with smallest perceived spending budget;
applying all models to customer data from the test set to obtain predictions for the spending for each model and customer;
converting the predictions into ranks and storing for each model in one or more electronic databases as a model ranking table, a number of ranking order switches relative to ranking of the observed customer spending being calculated for each model and ranking order switches being defined as those changes in ranking position of the prediction relative to the order of the true observations y;
calculating a measure of a magnitude of erroneous ranking from a weighted sum of ranking order switches;
transforming the number of ranking switches and weighted sum of ranking order switches into a range of \u22121, 1 wherein \u22121 corresponds to making all possible errors (inverse ranking) and 1 corresponds to a perfect model wherein said number of ranking switches has been transformed to represent a difference between a probability that the ranking of two customers andor potential customers are in the same order versus the probability that two of the customers andor potential customers are in different orders from the originally obtained rank;
normalizing transformed measures of order switches into a range of 0,1 wherein 1 corresponds to perfect ranking and 0 corresponds inverse ranking;
calculating a variance of measures of order switches and determining confidence intervals for each ranking measure, further comprising:
calculating using computing resources a variance that said measures of said ranking order switches,
wherein said calculating using computing resources a variance implements a relationship:
V
\u2062
a
^
\u2062
r
\u2061
(
\u03c4
^
)
=
(
2
n
\u2061
(
n
–
1
)
)
2
\xb7
2
\xb7
(
2
\u2062
\u2211
i
\u2062
\u2062
C
i
2
–
\u2211
i
\u2062
\u2062
C
i
–
(
2
\u2062
n
–
3
)
n
\u2061
(
n
–
1
)
\xb7
(
\u2211
i
\u2062
\u2062
C
i
)
2
)
wherein, a set of variables of said set of relationships includes:
C
i
=
\u2211
j
<
i
\u2062
\u2062
1
\u2062
{
y
i
>
y
j
}
+
\u2211
j
>
i
\u2062
\u2062
1
\u2062
{
y
i
<
y
j
}
is a number of observations that are concordant with observation i,
i is an integer from 1 to (n\u22121),
j is an integer from (i+1) to n,
n is a size of a set of customers andor potential customers, and
y is a predicted response of the model y=m(x);
calculating using computing resources a confidence interval for each measure of said ranking order switches; and
constructing using computing resources graphical representations of said ranking order switches;
updating the model performance table with the ranking measures and their confidence intervals; and
outputting the ranking measures and confidence levels as well as graphical representations thereof to a domain expert via electronic display on a computer monitor, who will choose based on this information the best model m* of the one or more models evaluated, and storing the predictions of y=m*(x) in the optimal prediction table.
2. The computer-implemented ranking-based method recited in claim 1, wherein the step of calculating a measure of a magnitude of erroneous ranking from a weighted sum of ranking order switches comprises the steps of:
calculating using computing resources a number of ranking order switches wherein ranking order switches are those changes in ranking order position of said plurality of customers andor potential customers assuming said ranking obtained in said obtaining step was erroneous, and
calculating using computing resources a weighted sum of ranking order switches wherein said weighted sum of ranking order switches provides a measure of the magnitude of erroneous ranking.
3. The computer-implemented ranking-based method of claim 2, wherein said calculating using computing resources the number of ranking order switches step comprises a relationship:
T=\u03a3i<j1{si>sj}
wherein, a set of variables of said relationship includes:
T is a number of rank order switches,
si is a rank order of one customer of said plurality of customers andor potential customers,
i is an integer from 1 to (n\u22121),
j is an integer from (i+1) to n, and
n is a size of a set of observed customers.
4. The computer-implemented ranking based method recited in claim 2, wherein said calculating using computing resources a weighted sum of order switches step comprising a relationship:
R=\u03a3i<j(j\u2212i)1{si>sj}
wherein, a set of variables of said relationship includes:
R is a weighted sum of order switches,
si is a rank order of one customer of said plurality of customers andor potential customers,
i is an integer from 1 to (n\u22121),
j is an integer from (i+1) to n, and
n is a size of a set of observed customers.
5. The computer-implemented ranking-based method recited in claim 1, wherein the step of outputting the ranking measures and confidence levels comprises the steps of:
providing an output that presents an updated ranking of said plurality of customers andor potential customers that corresponds to a model obtained in said applying step with the highest confidence interval of said all models; andor
providing an output of a model recommendation that corresponds to a best model of said all models evaluated.
6. The computer-implemented ranking-based method recited in claim 1, wherein the step of transforming said number of ranking switches and said weighting sum of order switches step implements a set of relationships:
\u03c4
^
=
1
–
4
\u2062
T
n
\u2061
(
n
–
1
)
,
\u2062
and
\u03c1
^
=
1
–
12
\u2062
R
n
\u2061
(
n
–
1
)
\u2062
(
n
+
1
)
wherein, a set of variables of said set of relationships includes:
{circumflex over (\u03c4)} is a probability that the ranking order has been changed,
n is a size of a set of customers andor potential customers,
{circumflex over (\u03c1)} is a ranking correlation,
T is a number of rank order switches, and
R is said weighted sum of order switches.
7. The computer-implemented ranking-based method recited in claim 1, wherein the step of outputting provides an updated ranking of a plurality of customers andor potential customers that corresponds to a model obtained in said applying step with the highest confidence interval of said all models.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method of enhancing the fungicidal activity of a triazole comprising adding N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane to a formulation comprising the triazole.
2. The method of claim 1 wherein the triazole is azaconazole, bromuconazole, cyproconazole, difenoconazole, epoxiconazole, fenbuconazole, fluquinconazole, flusilazole, hexaconazole, ketoconazole, metconazole, penconazole, propiconazole, tebuconazole, tetraconazole, or triticonazole.
3. The method of claim 2 wherein the triazole is propiconazole.
4. The method of claim 1 wherein the ratio of the triazole to the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 5:1 to 1:200.
5. The method of claim 4 wherein the ratio of the triazole to the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 2:1 to 1:2.
6. The method of claim 1 wherein the concentration of the triazole is from 0.01% (wv) to 10% (wv) and the concentration of the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 0.1% (wv) to 40% (wv).
7. The method of claim 6 wherein the concentration of the triazole is from 0.1% (wv) to 1.5% (wv) and the concentration of the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 0.5% (wv) to 10% (wv).
8. A method of protecting a plant or any part of a plant from a fungus comprising applying a composition comprising a triazole and N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane to the plant or any part of the plant.
9. The method of claim 8 wherein the triazole is azaconazole, bromuconazole, cyproconazole, difenoconazole, epoxiconazole, fenbuconazole, fluquinconazole, flusilazole, hexaconazole, ketoconazole, metconazole, penconazole, propiconazole, tebuconazole, tetraconazole, or triticonazole.
10. The method of claim 9 wherein the triazole is propiconazole.
11. The method of claim 8 wherein the ratio of the triazole to the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 5:1 to 1:200.
12. The method of claim 11 wherein the ratio of the triazole to the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 2:1 to 1:2.
13. The method of claim 8 wherein the concentration of the triazole is from 0.01% (wv) to 10% (wv) and the concentration of the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 0.1% (wv) to 40% (wv).
14. The method of claim 13 wherein the concentration of the triazole is from 0.1% (wv) to 1.5% (wv) and the concentration of the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 0.5% (wv) to 10% (wv).
15. A method for preserving wood, wood products, or biodegradable materials comprising applying a composition comprising a triazole and N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane to the wood, wood products, or biodegradable materials.
16. The method of claim 15 wherein the triazole is azaconazole, bromuconazole, cyproconazole, difenoconazole, epoxiconazole, fenbuconazole, fluquinconazole, flusilazole, hexaconazole, ketoconazole, metconazole, penconazole, propiconazole, tebuconazole, tetraconazole, or triticonazole.
17. The method of claim 16 wherein the triazole is propiconazole.
18. The method of claim 15 wherein the ratio of the triazole to the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 5:1 to 1:200.
19. The method of claim 18 wherein the ratio of the triazole to the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 2:1 to 1:2.
20. The method of claim 15 wherein the concentration of the triazole is from 0.01% (wv) to 10% (wv) and the concentration of the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 0.1% (wv) to 40% (wv).
21. The method of claim 20 wherein the concentration of the triazole is from 0.1% (wv) to 1.5% (wv) and the concentration of the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 0.5% (wv) to 10% (wv).
22. A composition comprising a fungicidal triazole and N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane.
23. The composition of claim 22 wherein the fungicidal triazole is propiconazole.
24. The composition of claim 22 wherein the ratio of the fungicidal triazole to the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 5:1 to 1:200.
25. The composition of claim 24 wherein the ratio of the fungicidal triazole to the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 2:1 to 1:2.
26. The composition of claim 22 wherein the concentration of the fungicidal triazole is from 0.01% (wv) to 10% (wv) and the concentration of the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 0.1% (wv) to 40% (wv).
27. The composition of claim 26 wherein the concentration of the fungicidal triazole is from 0.1% (wv) to 1.5% (wv) and the concentration of the N,N\u2032,N\u2032-tris(2-hydroxyethyl)-N-cocoalkyl-1,3-diamino-propane is from 0.5% (wv) to 10% (wv).