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Implement pre-built statistical models that can analyze match, player, and team-level data.
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Hi, respect for your awesome work! I have a question about the training. In backtracking stage, the generator's timestep is fixed to 399, and the timesteps of student and teacher are randomly sampled …
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My suggestions for 1.0 of the package, specifically for learning:
- [x] Abstract base class for the learning process. BetaEstimator and SignalOptimizer should inherit from this.
- [x] "Metric" ren…
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http://stackoverflow.com/questions/27651702/checking-for-multicollinearity-in-python
I don't think we have any ready made standalone functions either for an exog directly, of for the results (score…
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For regression models, fitted error models can be used to connect uncertainty scores based on distance-based contributions or ensemble variance to give confidence intervals. For classification models,…
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- [ ] [GPTScore: A Novel Evaluation Framework for Text Generation Models](https://github.com/jinlanfu/GPTScore?tab=readme-ov-file)
# GPTScore: A Novel Evaluation Framework for Text Generation Models
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Right now, setting `compute=True` triggers a sequential computation of the following steps:
1. the preprocessor scaler
2. optional NaN checks
3. SVD
4. scores and components
I’m starting to d…
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**Is your feature request related to a problem? Please describe.**
This project aims to predict the stock prices of Netflix using various machine learning techniques. Historical stock data is utilize…
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## 🚀 Feature
Add GEMBA, a GPT-based metric for assessment of translation quality, introduced in [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://arxiv.org/pdf/…
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**### Title: Model Evaluation and Comparison for Loan Prediction**
**Description:**
This issue pertains to the implementation and evaluation of various machine learning models for predicting loa…