kingychiu / target-permutation-importances
A Python Package that computes Target Permutation Importances (Null Importances) of a machine learning model.
☆14Updated 7 months ago
Related projects ⓘ
Alternatives and complementary repositories for target-permutation-importances
- Data, Benchmarks, and methods submitted to the M6 forecasting competition☆96Updated last month
- A python module that uses hill climbing to iteratively blend machine learning model predictions.☆43Updated 6 months ago
- An extension of LightGBM to probabilistic modelling☆274Updated 5 months ago
- ☆167Updated 3 years ago
- Time Series Forecasting with LightGBM☆80Updated 2 years ago
- M6-Forecasting competition☆42Updated 9 months ago
- Linear Prediction Model with Automated Feature Engineering and Selection Capabilities☆500Updated last month
- Scikit-learn compatible implementation of the Gauss Rank scaling method☆73Updated last year
- A power-full Shapley feature selection method.☆198Updated 6 months ago
- All Relevant Feature Selection☆118Updated last month
- Fast Combinatorial Cross Validation☆10Updated 3 years ago
- A Tensorflow 2.0 implementation of TabNet.☆239Updated last year
- An extension library for NumPy that implements common array operations not present in NumPy☆42Updated 11 months ago
- A small library to locally calculate the scores on numer.ai tournament's diagnostics dashboard.☆36Updated 3 years ago
- Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.☆132Updated 2 months ago
- the first place solution of JPX fundamentals analysis challenge☆41Updated 2 years ago
- X-Trend: Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies☆61Updated 8 months ago
- Scikit-learn style cross-validation classes for time series data☆272Updated 2 years ago
- Time Series Cross-Validation -- an extension for scikit-learn☆247Updated last year
- Utilities and information for the signals.numer.ai tournament☆24Updated last year
- My toolbox for data analysis. :)☆175Updated this week
- An implementation of the focal loss to be used with LightGBM for binary and multi-class classification problems☆243Updated 5 years ago
- Technical Analysis Indicators for polars☆114Updated this week
- Probabilistic Sharpe Ratio example in Python (by Marcos López de Prado)☆120Updated 4 years ago
- Fast window operations☆38Updated 5 months ago
- Forecasting with Gradient Boosted Time Series Decomposition☆187Updated last year
- Optuna + LightGBM = OptGBM☆35Updated 2 years ago
- Quantile Regression Forests compatible with scikit-learn.☆211Updated this week
- Gradient Boosting With Piece-Wise Linear Trees☆149Updated 6 months ago