Fake-News-Classification
Astock
| Fake-News-Classification | Astock | |
|---|---|---|
| 1 | 1 | |
| 0 | 190 | |
| - | - | |
| 10.0 | 3.9 | |
| about 2 years ago | 10 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | - |
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Fake-News-Classification
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On Data Quality
For our capstone project at Flatiron school we had to not only pitch the project we wanted to do, but also find a dataset that would allow us to accomplish that project. I chose to make a Fake News Classifier, and pitched a dataset I found on Kaggle for it. My instructor was quick in turning it down. It had no information on how that data was acquired, how it was labelled, and I had no means of verifying it. With some more research done I found the Liar dataset, which contained thousands of data points, humanly labelled by editors from politifact.com using a truthiness scale and which contained extensive metadata on each instance, making it verifiable. Once I settled on my final model, I decided to train a version of it on the rejected dataset, just for curiosity. The Accuracy it provided for the test data from that dataset was way higher than the one trained in the Liar dataset. Why was that? The model wasn't actually making correct predictions, it was just better at identifying the labels (which were not verified) from the dataset.
Astock
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