The article discusses the credibility of using Random Forest Variable Importance for identifying causal links in data where the output is binary. It contrasts this method with fitting a Logistic Regression model and examining its coefficients. The discussion highlights the challenges of extracting causality from observational data without controlled experiments, emphasizing the importance of domain knowledge and the use of partial dependence plots for interpreting model results.
An article detailing how to build a flexible, explainable, and algorithm-agnostic ML pipeline with MLflow, focusing on preprocessing, model training, and SHAP-based explanations.
This article explores how multi-collinearity can damage causal inferences in marketing mix modeling and provides methods to address it, including Bayesian priors and random budget adjustments.
Discover how AutoGluon, an open-source machine learning library developed by Amazon Web Services, automates the entire ML pipeline, including data preprocessing, feature engineering, model training, and evaluation. With just four lines of code, learn how AutoGluon delivers top-notch performance by employing techniques like ensemble learning and automatic hyperparameter tuning.
PySpark for time-series data, discussing data ingestion, extraction, and visualization with practical implementation code.
This article provides a comprehensive guide to performing exploratory data analysis on time series data, with a focus on feature engineering.
