How to implement 10 fold cross validation?
I have a code to perform 10 fold cross-validation on a dataset. The code is created by dividing the data into k-1 parts for training and the remaining part for testing. I want to see if my code is correct or not
My code is:
Dataset data = data1;
Dataset folds = data.folds((10), new Random(100));
Dataset training = new DefaultDataset();
Dataset testing = new DefaultDataset();
int tr = {0, 2, 3, 4,5, 6,7, 8, 9};
int te = {1};
for (int i = 0; i < tr.length; i++) {
training.addAll(folds[tr[i]]);
}
for (int i = 0; i < te.length; i++) {
testing.addAll(folds[te[i]]);
}
java machine-learning cross-validation
edited Nov 18 '18 at 8:26
desertnaut
17k63668
asked Nov 18 '18 at 2:49
Data MinerData Miner
1116
add a comment |
I have a code to perform 10 fold cross-validation on a dataset. The code is created by dividing the data into k-1 parts for training and the remaining part for testing. I want to see if my code is correct or not
My code is:
Dataset data = data1;
Dataset folds = data.folds((10), new Random(100));
Dataset training = new DefaultDataset();
Dataset testing = new DefaultDataset();
int tr = {0, 2, 3, 4,5, 6,7, 8, 9};
int te = {1};
for (int i = 0; i < tr.length; i++) {
training.addAll(folds[tr[i]]);
}
for (int i = 0; i < te.length; i++) {
testing.addAll(folds[te[i]]);
}
java machine-learning cross-validation
edited Nov 18 '18 at 8:26
desertnaut
17k63668
asked Nov 18 '18 at 2:49
Data MinerData Miner
1116
add a comment |
I have a code to perform 10 fold cross-validation on a dataset. The code is created by dividing the data into k-1 parts for training and the remaining part for testing. I want to see if my code is correct or not
My code is:
Dataset data = data1;
Dataset folds = data.folds((10), new Random(100));
Dataset training = new DefaultDataset();
Dataset testing = new DefaultDataset();
int tr = {0, 2, 3, 4,5, 6,7, 8, 9};
int te = {1};
for (int i = 0; i < tr.length; i++) {
training.addAll(folds[tr[i]]);
}
for (int i = 0; i < te.length; i++) {
testing.addAll(folds[te[i]]);
}
java machine-learning cross-validation
edited Nov 18 '18 at 8:26
desertnaut
17k63668
asked Nov 18 '18 at 2:49
Data MinerData Miner
1116
I have a code to perform 10 fold cross-validation on a dataset. The code is created by dividing the data into k-1 parts for training and the remaining part for testing. I want to see if my code is correct or not
My code is:
Dataset data = data1;
Dataset folds = data.folds((10), new Random(100));
Dataset training = new DefaultDataset();
Dataset testing = new DefaultDataset();
int tr = {0, 2, 3, 4,5, 6,7, 8, 9};
int te = {1};
for (int i = 0; i < tr.length; i++) {
training.addAll(folds[tr[i]]);
}
for (int i = 0; i < te.length; i++) {
testing.addAll(folds[te[i]]);
}
java machine-learning cross-validation
java machine-learning cross-validation
edited Nov 18 '18 at 8:26
desertnaut
17k63668
asked Nov 18 '18 at 2:49
Data MinerData Miner
1116
edited Nov 18 '18 at 8:26
desertnaut
17k63668
asked Nov 18 '18 at 2:49
Data MinerData Miner
1116
edited Nov 18 '18 at 8:26
desertnaut
17k63668
edited Nov 18 '18 at 8:26
desertnaut
17k63668
edited Nov 18 '18 at 8:26
desertnaut
17k63668
17k63668
asked Nov 18 '18 at 2:49
Data MinerData Miner
1116
asked Nov 18 '18 at 2:49
Data MinerData Miner
1116
asked Nov 18 '18 at 2:49
Data MinerData Miner
1116
1116
add a comment |
add a comment |
1 Answer
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oldest
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Assuming code in the line
data.folds((10), new Random(100));
is correctly sampling 10 percent of the data, and return all 10 folds to output variable, separating the dataset looks correct.
However, you should remember to iterate k times for k-fold cross validation and average results.
Source: https://en.wikipedia.org/wiki/Cross-validation_(statistics)#/media/File:K-fold_cross_validation_EN.jpg
answered Nov 18 '18 at 5:16
Semih KorkmazSemih Korkmaz
8191024
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Assuming code in the line
data.folds((10), new Random(100));
is correctly sampling 10 percent of the data, and return all 10 folds to output variable, separating the dataset looks correct.
However, you should remember to iterate k times for k-fold cross validation and average results.
Source: https://en.wikipedia.org/wiki/Cross-validation_(statistics)#/media/File:K-fold_cross_validation_EN.jpg
answered Nov 18 '18 at 5:16
Semih KorkmazSemih Korkmaz
8191024
add a comment |
Assuming code in the line
data.folds((10), new Random(100));
is correctly sampling 10 percent of the data, and return all 10 folds to output variable, separating the dataset looks correct.
However, you should remember to iterate k times for k-fold cross validation and average results.
Source: https://en.wikipedia.org/wiki/Cross-validation_(statistics)#/media/File:K-fold_cross_validation_EN.jpg
answered Nov 18 '18 at 5:16
Semih KorkmazSemih Korkmaz
8191024
add a comment |
Assuming code in the line
data.folds((10), new Random(100));
is correctly sampling 10 percent of the data, and return all 10 folds to output variable, separating the dataset looks correct.
However, you should remember to iterate k times for k-fold cross validation and average results.
Source: https://en.wikipedia.org/wiki/Cross-validation_(statistics)#/media/File:K-fold_cross_validation_EN.jpg
answered Nov 18 '18 at 5:16
Semih KorkmazSemih Korkmaz
8191024
Assuming code in the line
data.folds((10), new Random(100));
is correctly sampling 10 percent of the data, and return all 10 folds to output variable, separating the dataset looks correct.
However, you should remember to iterate k times for k-fold cross validation and average results.
Source: https://en.wikipedia.org/wiki/Cross-validation_(statistics)#/media/File:K-fold_cross_validation_EN.jpg
answered Nov 18 '18 at 5:16
Semih KorkmazSemih Korkmaz
8191024
answered Nov 18 '18 at 5:16
Semih KorkmazSemih Korkmaz
8191024
answered Nov 18 '18 at 5:16
Semih KorkmazSemih Korkmaz
8191024
answered Nov 18 '18 at 5:16
Semih KorkmazSemih Korkmaz
8191024
8191024
add a comment |
add a comment |
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