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When people think about machine learning, they often focus on advanced algorithms such as neural networks or random forests. However, some of the most useful predictive models are built upon well established statistical techniques. One example is the Generalised...Blog
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...Blog
Exponential Smoothing is a forecasting algorithm used to analyse 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...Blog
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...Blog
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...Blog
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...
