Row 88955

Row ID: 88955 | Dataset Entry | Axioma AXP Content Repository

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I am preparing my reserch paper about using ai and voice recordings to detect parkinson at early stages.

I finished the code now, I create a machine model to detect parkinson. I used a dataset with **756 features**.

here's the steps:

* step 1: **knn** `(n_neighbors=5, p=2)`: accuracy 84.11 f1 89.66 * step 2: **knn** `(n_neighbors=1, p=1)`: accuracy 95.39 f1 96.96 * step 3: **knn** `(n_neighbors=1, p=1)` **+ cross validation 5-fold** : mean accuracy 96.19 +/- 1.14 mean f1 97.45 +/- 0.76. * step 4: **bagging + knn** `(n_neighbors=1, p=1)` accuracy 94.74 * step 5: **bagging**`(max_features=0.37, n_estimators=20)` + **knn**`(n_neighbors=1, p=1)`: accuracy 96.71 f1 97.84 * step 6: **bagging**`(max_features=0.37, n_estimators=20)` + **knn**`(n_neighbors=1, p=1)` + **cross validation 5-folds**: mean accuracy 97.22 +/- 0.78 mean f1 98.15 +/- 0.52

The scores are higher than previously published research papers that used the same dataset I used.

The problem is that I made a research online and found that knn with n\_neighbors=1 is not reliable.

I did extensive reserch of previously published reserch papers and found a research papers that used **knn with k=1** and the research paper is peer review. **Is it safe then to work with k=1 because it's already worked with in a previously peer reviewed and published research ?**

FieldValue
text I am preparing my reserch paper about using ai and voice recordings to detect parkinson at early stages. I finished the code now, I create a machine model to detect parkinson. I used a dataset with **756 features**. here's the steps: * step 1: **knn** `(n_neighbors=5, p=2)`: accuracy 84.11 f1 89.66 * step 2: **knn** `(n_neighbors=1, p=1)`: accuracy 95.39 f1 96.96 * step 3: **knn** `(n_neighbors=1, p=1)` **+ cross validation 5-fold** : mean accuracy 96.19 +/- 1.14 mean f1 97.45 +/- 0.76. * ste…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-25
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Raw Record

{
  "text": "I am preparing my reserch paper about using ai and voice recordings to detect parkinson at early stages.\n\nI finished the code now, I create a machine model to detect parkinson. I used a dataset with **756 features**.\n\nhere's the steps:\n\n* step 1: **knn** `(n_neighbors=5, p=2)`: accuracy 84.11 f1 89.66\n* step 2: **knn** `(n_neighbors=1, p=1)`: accuracy 95.39 f1 96.96\n* step 3: **knn** `(n_neighbors=1, p=1)` **+ cross validation 5-fold** : mean accuracy 96.19 +/- 1.14 mean f1 97.45 +/- 0.76.\n* step 4: **bagging + knn** `(n_neighbors=1, p=1)` accuracy 94.74\n* step 5: **bagging**`(max_features=0.37, n_estimators=20)` + **knn**`(n_neighbors=1, p=1)`: accuracy 96.71 f1 97.84\n* step 6: **bagging**`(max_features=0.37, n_estimators=20)` + **knn**`(n_neighbors=1, p=1)` + **cross validation 5-folds**: mean accuracy 97.22 +/- 0.78 mean f1 98.15 +/- 0.52\n\nThe scores are higher than previously published research papers that used the same dataset I used.\n\nThe problem is that I made a research online and found that knn with n\\_neighbors=1 is not reliable.\n\nI did extensive reserch of previously published reserch papers and found a research papers that used **knn with k=1** and the research paper is peer review. **Is it safe then to work with k=1 because it's already worked with in a previously peer reviewed and published research ?**",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-25",
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}
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