This course presents advanced models to predict categorical and continuous targets. Before reviewing the models, data preparation issues are addressed such as partitioning, detecting anomalies, and balancing data. You are first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core fields, referred to as components or factors. The next units focus on supervised models, including Decision List, Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed to combine supervised models and execute them in a single run, both for categorical and continuous targets.
On-demand duration is 8 hours.
Sınıf eğitimlerimizi Kıbrıs ofislerimizde düzenlemekteyiz. Kurumunuza özel eğitimleri ise, dilediğiniz tarih ve lokasyonda organize edebiliriz.