Explainable T learner Deep Learning Uplift Model Using Python Package CausalML | Machine Learning
T-learner is a meta-learner that uses two machine learning models to estimate the individual-level heterogeneous causal treatment effect. In this tutorial, we will talk about how to use the python package causalML to build a T-learner. We will cover:
👉 How to implement T-learner using the XGBoost model, the light GBM model, and the neural network model separately?
👉 How to make individual treatment effect (ITE) and average treatment effect (ATE) estimations using a T-learner?
👉 How to check the T-learner feature importance?
👉 How to interpret a T-learner uplift model using SHAP?
⏰ Timecodes ⏰
0:00 - Intro
0:38 - Step 1: Install and Import Libraries
1:32 - Step 2: Create Dataset
2:07 - Step 3: T-learner Using XGBoost Model
3:04 - Step 4: T-learner Using Light GBM Model
4:26 - Step 5: T-learner Using Neural Network Model
5:57 - Step 6: T-learner Neural Network Model Feature Importance
7:10 - Step 7: T-learner Neural Network Model Interpretation
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En esta página del sitio puede ver el video en línea Explainable T learner Deep Learning Uplift Model Using Python Package CausalML | Machine Learning de Duración hora minuto segunda en buena calidad , que subió el usuario Grab N Go Info 06 octubre 2022, comparta el enlace con amigos y conocidos, en youtube este video ya ha sido visto 828 veces y le gustó 6 a los espectadores. Disfruta viendo!
