This dataset is publicly available for the research purpose. We are going use the Titanic passenger dataset and their information to design our Machine Learning model and test the designed model to see whether it can accurately find out whether a passenger will survive or not from the previous provided data. This sensational tragedy shocked the international community and led to the better safety regulations for the ships. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. In this section, we will design a real life machine learning Application, using an open source Titanic Survival dataset to predict the survival of passengers according to the provided data. Two-Class Decision Jungle Application in Azure ML Model selection solely depends on the problem, dataset, features and our priorities.įigure: Two-class decision jungle model architecture in Azure ML Studio If the dataset has overlapping features – the feature values and nature are similar, then we can also consider Two-class boosted decision tree model. If our primary preference is speed of the system, then we should consider Two-Class Averaged Perception model but, if our preference is accuracy, then Two-Class decision jungle model is a preferable choice. It solves classification problems and it can handle more than 100 k data points and the number of features must be less than 100. If we want to take a decision between the two choices, using provided features, then we should use Two-class classification decision jungle to design our model. In Azure Machine Learning Studio, we usually use two-class or multi-class classification decision jungle to predict future datapoint categories.
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