Describe results and performance of the svm classifier

Reference no: EM131725656

1.0 Choose a dataset

Format the data and load into WEKA. Make sure the dataset is appropriate for the meta-algorithm: Bagging.Bagging works on top of another kind of classifier.

You can pick an appropriate type of classifier to use within Bagging.

Describe:
1 The dataset, including
1.1 The data that's been collected
1.2 The features you'll use
1.3 The outcomes you're trying to predict
1.4 The reasons for the classification problem you're trying to solve

2.2 Train and evaluate:
2.1 An SVM classifier
2.2 A Bagging classifier

2.3Describe your first-pass analysis of the data, including selected scatter plots.

3.0 Describe the classification method: BAGGING

Including:
3.1 How it works: CLEAR AND UNDERSTANDABLE EXPLANATION
3.2 Assumptions the method makes
3.3 Strengths
3.4 Weaknesses
3.5 What kind of data works best with bagging

4.0 Describe results and performance of the SVM classifier and the Bagging classifier. Compare the results and recommend one of them explaining clearly why you prefer that one.

Reference no: EM131725656

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