by Gabriel Tam | Jun 13, 2026 | by Gabriel Tam, BTS Consulting
Support Vector Machine (SVM) is a supervised machine learning algorithm that can be applied to both classification and regression problems. Its purpose is to detect patterns within data and make predictions by identifying the most effective boundary between different...
by Gabriel Tam | Jun 6, 2026 | BTS Consulting, by Gabriel Tam
Exponential Smoothing is a forecasting algorithm used to analyze time based data and predict future values. It’s particularly useful when data is collected over regular intervals, such as daily sales figures, monthly revenue, or weekly demand. The algorithm works by...
by Gabriel Tam | May 30, 2026 | BTS Consulting, by Gabriel Tam
Naive Bayes is a supervised machine learning algorithm consisting of three main types (Gaussian, Multinomial & Bernoulli). This probability-based algorithm is mainly used for classification tasks by using the relationships between different features to predict the...
by Gabriel Tam | May 23, 2026 | BTS Consulting, by Gabriel Tam
Random Forest is a supervised machine learning algorithm used for both classification and regression tasks. It is based on the idea of combining multiple decision trees to produce a stronger and more reliable model. Instead of relying on a single tree, Random Forest...
by Gabriel Tam | May 16, 2026 | BTS Consulting, by Gabriel Tam
Minimum Description Length, often shortened to MDL, is a principle used in machine learning and data analysis to help identify models that best explain a dataset while avoiding unnecessary complexity. The main idea behind MDL is that the best model is one that...
