BabuKaushik10's repositories
Liquefaction-XGBoost-SHAP-Jas-Dodagoudar
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.
awesome-machine-learning-interpretability
A curated list of awesome machine learning interpretability resources.
AI101
Analytics Club Sessions 2022
ALEPython
Python Accumulated Local Effects package
alibi
Algorithms for explaining machine learning models
anchor
Code for "High-Precision Model-Agnostic Explanations" paper
awesome-gradient-boosting-papers
A curated list of gradient boosting research papers with implementations.
CTGAN
Conditional GAN for generating synthetic tabular data.
deep-learning-keras-tf-tutorial
Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow tutorials, tensorflow 2.0 tutorial. deep learning tutorial python.
DiCE
Generate Diverse Counterfactual Explanations for any machine learning model.
Fooling-LIME-SHAP
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
GAN-for-tabular-data
We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.
iml
iml: interpretable machine learning R package
interpret
Fit interpretable models. Explain blackbox machine learning.
Interpretable-Machine-Learning-with-Python
Interpretable Machine Learning with Python, published by Packt
kmeans_smote
Oversampling for imbalanced learning based on k-means and SMOTE
LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
lime
Lime: Explaining the predictions of any machine learning classifier
Liquefaction-gravel-eml-2023
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.
Machine-Learning-Collection
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)
Pullout-coefficient_XGBoost
User interface to predict pullout interaction coefficients of geogrid through XGBoost model
py
Repository to store sample python programs for python learning
Python-
Python for beginners
python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
SDMetrics
Metrics to evaluate quality and efficacy of synthetic datasets.
shap
A game theoretic approach to explain the output of any machine learning model.
xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
YouTube-Tutorials-1
Repo containing scripts for videos featured on Adrian Dolinay's YouTube channel.