FastML – An Embarrassingly Powerful Solution
Introduction:
In August, we released the goodbooks-10k dataset, a revolutionary dataset for book recommendations. We were hopeful that our dataset would be recognized by Kaggle for its quality, impact, and reach. However, the Kaggle team chose datasets that were surprising, to say the least. While there is nothing wrong with the chosen datasets, we couldn’t help but wonder if there were better alternatives. The winning dataset focused on a simulation of a robot holding a ball, while the runner up datasets included cryptocurrency historical prices and a collection of favicons. Our dataset, which offers unique and valuable book recommendations, went unnoticed. But, the good news is that the datasets prizes will now be awarded monthly, and we are eager to participate in future competitions.
Full Article: FastML – An Embarrassingly Powerful Solution
Kaggle Dataset Awards Overlook Unique and Impactful Datasets
In August, a new dataset called goodbooks-10k was introduced for book recommendations, coinciding with the announcement of the Kaggle Datasets Awards. Despite high hopes, the winning datasets chosen by Kaggle seemed less than extraordinary. Let’s explore the criteria they used to make their selections and examine the actual winners.
Criteria for Kaggle Dataset Awards
Kaggle emphasized three main criteria for their dataset awards: quality, impact, and reach. These factors were meant to highlight the standout datasets that were making a significant contribution to the field of data science.
The Actual Winners
The dataset that attracted the jury’s attention the most was a simulation of a robot holding a ball. This dataset focused on the kinematics of a simulated robot arm and showcased the combination of robotics and deep learning. While the dataset only contained 20 numerical columns, its potential for further research in these exciting fields was evident. Despite receiving only three likes and 24 downloads after the announcement, the unique nature of this dataset made it an unexpected winner.
The second winning dataset, “Cryptocurrency Historical Prices,” provided valuable information about the price history of various cryptocurrencies. Although some may argue that similar data is readily available from other sources, this dataset stood out for its frequency of updates and availability across different formats. It garnered more attention and downloads compared to the first and third winners.
The third chosen dataset might appear surprising to some. It consisted of a collection of favicons, the tiny icons that browsers use to represent websites. However, Kaggle justified its selection by suggesting that there are opportunities to explore image processing and computer vision techniques using this dataset. With 778 MB of favicons, this dataset sparked interest within the community, as evidenced by six likes in total.
Overlooked Competitors
Among the datasets that seemed like potential competitors to goodbooks-10k were “US Household Income Stats Geo Locations,” “All the News,” and “515k Hotel Reviews Data in Europe.” These datasets showcased originality, received notable downloads and likes, and had the potential to make a significant impact. Additionally, SURECOMMENDER’s efforts to classify cervical cancer risk and the subsequent enjoyable journey were worth considering as well.
Further Disappointments
To add insult to injury, goodbooks-10k didn’t even receive a mention, but a notebook and recommender built on this dataset were selected for a weekly kernel award the following day. This oversight was disheartening for those involved in creating the original dataset.
Exciting Future Prospects
Despite the initial disappointment, there is good news on the horizon. The Kaggle Dataset Awards, initially advertised as a one-time event, will now be awarded monthly until the end of the year. This announcement has rekindled interest and enthusiasm among the data science community, with many eagerly preparing to participate in future rounds.
Conclusion
While the winners of the Kaggle Dataset Awards may not have aligned with the expectations of some, the criteria used by Kaggle should be acknowledged. The pursuit of groundbreaking and impactful datasets remains at the core of data science. Moving forward, researchers, enthusiasts, and data scientists are encouraged to continue pushing the boundaries and submitting remarkable datasets for consideration in future Kaggle competitions.
Summary: FastML – An Embarrassingly Powerful Solution
In August, we introduced the goodbooks-10k dataset for book recommendations, coinciding with the Kaggle Datasets Awards. However, the chosen datasets by Kaggle were unexpected and left us amazed. Nonetheless, let’s understand the criteria for their selection: quality, impact, and reach. The winners included a simulation dataset on robot arm kinematics combined with deep learning, a dataset on cryptocurrency historical prices, and a collection of website favicons. Despite our dataset not being mentioned, a notebook and recommender using it received a weekly kernel award. Moving forward, Kaggle will continue to award datasets monthly until the end of the year. Exciting times ahead!
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