Tag: bigml
Model Evaluation Fundamentals: How to Know if Your ML Model Actually Works
8/30/2025Master the essential techniques for evaluating machine learning models - from confusion matrices to ROC curves and beyond.
Model Tuning & Hyperparameters: From Grid Search to Automated Optimization
8/30/2025Transform underperforming models into production-ready systems through systematic hyperparameter optimization and automated tuning strategies.
Operating Models & Thresholds: Business-Aligned Decision Making
8/30/2025Transform model probabilities into business decisions through optimal threshold selection, cost-sensitive learning, and operating point optimization.
Time Series Forecasting: From Trends to Business Predictions
8/30/2025Master time series forecasting with exponential smoothing, trend analysis, and seasonality detection for accurate business predictions.
Train/Test Split vs Cross-Validation: Robust Model Validation Strategies
8/30/2025Master the essential techniques for splitting data and validating models - from simple holdouts to stratified k-fold cross-validation.
Classification vs Regression: Choose the Right Target
8/23/2025Understand the difference between classification and regression and how to pick metrics and models.
Evaluations: Metrics, Validation, and Error Analysis
8/23/2025How to choose metrics, validate models, and run error analysis to improve ML performance.
Our First ML Model: Decision Tree from Scratch
8/23/2025Hands-on: train a small decision tree, understand splits, and measure performance.
How to Train Models: From Problem to Production
8/23/2025A practical guide that walks through the steps to train ML models reliably and repeatably.
ML Steps & High-Value Use Cases
8/23/2025Map ML pipeline steps to common business use cases and priorities for impact.
Supervised Learning: Concepts, Patterns, and Pitfalls
8/23/2025A practical guide to supervised learning, including common algorithms and how to avoid mistakes.
What's Machine Learning? A Practical Introduction
8/23/2025An accessible, practical introduction to machine learning for analysts and engineers.