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Super Vector Machine(SVM) is a set of supervised learning methods used for classification, regression, and other detection.
a) What makes SVM different with other machine learning models?
b) Perform dataset creation as instructed in the code below:
c) Using the dataset you created before, perform training and testing using two different tuning sets of linear kernel SVM model. Train and test ratio is 80:20.
d)Using the dataset you created before, perform training and testing using two different tuning sets of Radial Basis Function (RBF) SVM model. Train and test ratio is 80:20.
e) Based on experiments 2c and 2d complete the following table:
f) Perform analysis based on obtained results in Table 2.2. (Sugesstion: discuss which one is the best, then why, which one is the worst, then why.)
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The idea of ensemble learning is to build a prediction model by combining the strengths of a collection of simpler base models, called weak learners. Bagging is one of ensemble learning. In this question, we will compare a single decision tree model with a bagging model containing 100 decision trees. The HeartFailurePredictionDataset.csv dataset used can be seen below:
a) Perform feature engineering into our dataset.
b) Using the HeartFailurePredictionDataset dataset, perform training and testing using a single decision tree model. Train and test ratio is 80:20.
c) Using the HeartFailurePredictionDataset.csv dataset, perform training and testing using the Bagging model which contain 100 decision tree models. Train and test ratio is 80:20.
d) Based on experiments 3b and 3c, complete the following table:
e) Perform analysis based on obtained results in Table 3.1. (Sugesstion: discuss which one is the best, then why, which one is the worst, then why.)