What is the support vector machine classifier? A practical extension of an ideal classification rule – where the ideal case achieves zero prediction errors, but the extension must compromise via minimal error. The literature calls that ideal classification rule, the “optimal separating hyperplane.” This optimal separating hyperplane must be understood first, before extending to the support vector machine.
What’s your favorite restaurant? It’s probably your favorite because of current experience – great food, atmosphere, value, and so on. But how do you anticipate future experience? How do you assess where a restaurant is trending?
A restaurant’s trend hints at future direction: what might it be like in 6 months or 1 year?
Picture this. Your mission is to secure great deals on vehicles sold at wholesale auto auction. An intriguing prospect comes on your radar. How do you make the purchasing decision? How do you quickly weigh 25 pieces of available information? I’m eager to help. I introduce and verify a machine learning model trained by 70,000 historical transactions.
.nobullet li { list-style-type: none; } Key Takeaways
A business case study may be played out using a replicable controlled experiment1.
Consider a hypothetical business case with these characteristics:
The objective is prediction of a numerical outcome. Many indicators are available to try and predict the outcome.
Key Takeaways
A linear model – a “line of best fit” – estimates the true relationship between predictors and an outcome.
Linear models come in classical statistics or machine learning varieties.
To learn about linear models’ relative performance/behavior, we use controlled experiments, powered by computer simulation.