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From the selected samples, the model correctly predicts 7 out of 10 applications. In other words, for 7 applications, the patentability score is .5 or above when the USPTO accepted the application, otherwise it is less than .5. This tracks with the .73 accuracy metric from training. The model seems to do a little better with correctly predicting accepted applications than rejected ones, which is understandable given the slight skew in the data I used to train it.
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## Landing Page
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[https://sites.google.com/nyu.edu/dmb443-cs-gy-6613-project](https://sites.google.com/nyu.edu/dmb443-cs-gy-6613-project)
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# Milestone 3
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From the selected samples, the model correctly predicts 7 out of 10 applications. In other words, for 7 applications, the patentability score is .5 or above when the USPTO accepted the application, otherwise it is less than .5. This tracks with the .73 accuracy metric from training. The model seems to do a little better with correctly predicting accepted applications than rejected ones, which is understandable given the slight skew in the data I used to train it.
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## Landing Page and Demo Video
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[https://sites.google.com/nyu.edu/dmb443-cs-gy-6613-project](https://sites.google.com/nyu.edu/dmb443-cs-gy-6613-project)
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# Milestone 3
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