I will explain machine learning models using shap and feature importance
About this Gig
I provide explainable AI solutions to help understand how machine learning models make predictions. Model interpretability is essential for trust, transparency, and decision making.
I specialise in explaining complex models using SHAP and feature importance techniques.
Services include model interpretation, feature impact analysis, and visualisation of how variables influence predictions.
Techniques used:
SHAP values, feature importance, partial dependence analysis, and model interpretation methods.
Supported models:
Random Forest, XGBoost, LightGBM, CatBoost, Neural Networks, and other machine learning models.
Tools:
Python, SHAP, Scikit-learn, XGBoost, LightGBM, Jupyter Notebook, Amazon Sagemaker, Google Colab.
Please contact me before placing an order to discuss your model and interpretation requirements.

