Bridging Business Intelligence and Machine Learning: A Strategic Imperative
The fusion of Business Intelligence and Machine Learning offers a pathway from historical analysis to predictive and prescriptive decision-making.
Recent posts
Understanding the Differences Between ROC AUC and Precision-Recall AUC in Machine Learning
Explore the differences between ROC AUC and Precision-Recall AUC in machine learning and learn when to use each metric for classification tasks.
Entropy in Data Science and Machine Learning: A Deep Dive
Explore the deep connection between entropy, data science, and machine learning. Understand how entropy drives decision trees, uncertainty measures, feature selection, and information theory in modern AI.
Optimizing Machine Learning Models using Simulated Annealing
Discover how simulated annealing, inspired by metallurgy, offers a powerful optimization method for machine learning models, especially when dealing with complex and non-convex loss functions.
How to Write the Sample Size Justification Section in Your Clinical Protocol
A complete guide to writing the sample size justification section for your clinical trial protocol, covering key statistical concepts like power, error thresholds, and outcome assumptions.
Improving Decision Tree Performance with Genetic Algorithms
A deep dive into using Genetic Algorithms to create more accurate, interpretable decision trees for classification tasks.
Validating Anomaly Detection Models: Lessons from COPOD
COPOD is a popular anomaly detection model, but how well does it perform in practice? This article discusses critical validation issues in third-party models and lessons learned from COPOD.
Solving Data Drift Issues in Credit Risk Models
A comprehensive exploration of data drift in credit risk models, examining practical methods to identify and address drift using multivariate techniques.
The Unseen Art of Data Quality: Bridging the Gap Between Collection and Utilization
This article explores the often-overlooked importance of data quality in the data industry and emphasizes the urgent need for defined roles in data design, collection, and quality assurance.
Deciphering Cloud Customer Behavior
Understand how Markov chains can be used to model customer behavior in cloud services, enabling predictions of usage patterns and helping optimize service offerings.