Preregistering Structural Equation Modeling (SEM) Studies: A Comprehensive Guide
Learn how to preregister your SEM study by systematically locking down modeling and analytic decisions to improve scientific transparency and reduce bias.
Recent posts
Smarter Tree Splits: Understanding Friedman MSE in Regression Trees
Explore the smarter way of splitting nodes in regression trees using Friedman MSE, a computationally efficient and numerically stable alternative to classic variance-based MSE.
Why SMOTE Isn’t Always the Answer
SMOTE generates synthetic samples to rebalance datasets, but using it blindly can create unrealistic data and biased models.
Why Data Ethics Matters in Machine Learning
Ethical considerations are critical when deploying machine learning systems that affect real people.
Model Deployment: Best Practices and Tips
Deploying machine learning models to production requires planning and robust infrastructure. Here are key practices to ensure success.
Hyperparameter Tuning Strategies
Hyperparameter tuning can drastically improve model performance. Explore common search strategies and tools.
A Gentle Introduction to Neural Networks
Neural networks power many modern AI applications. This article introduces their basic structure and training process.
ARIMA Modeling in Python: A Quick Start Guide
A practical introduction to building ARIMA models in Python for reliable time series forecasting.
Crafting Time Series Features for Better Models
Learn specialized feature engineering techniques to make time series data more predictive for machine learning models.
Why Data Scientists Need Math and Statistics
Mastering mathematics and statistics is essential for understanding data science algorithms and avoiding common pitfalls when building models.