Andrea Loreggia (@aloreggia)
github

aloreggia

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  • Stars
    star
    5
  • Global Rank 1,431,029 (Top 50 %)
  • Followers 1
  • Following 1
  • Registered almost 10 years ago
  • Most used languages
    Python
         66.7 %
    Jupyter Notebook
         33.3 %
  • Location 🇮🇹 Italy
  • Country Total Rank 9,881
  • Country Ranking
    Jupyter Notebook
         1,439
    Python
         2,409

Top repositories

1

vorace

In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique that does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.
Python
4
star
2

sofai

This code has been generated by Andrea Loreggia in the context of an IBM-led project on Thinking Fast and Slow in AI
Jupyter Notebook
1
star
3

Cpnet-dist

Python
1
star