Intelligent Systems Weka Assignment – DM ML - IT Computer Science Assignment Help

Assignment Task:

Task:

Data Mining with WEKA

This assignment allows you to make a variety of unconstrained choices so different students should be making different combinations of choices! You may choose to undertake this assignment in a supervised paradigm (learning and testing rules or classes using tagged datasets) or an unsupervised paradigm (learning and comparing rules or classes learned using untagged datasets). Where information from 12 independent distributions with finite mean and variance is summed (or averaged) in a standardized way, the resulting distribution approximates the normal distribution irrespective of the original distributions (Central Limit Theorem). So we define the framework of our Machine Learning/Data Mining exploration as follows:

1. Choose and justify choice of dataset and algorithm for study (20%)

1a. Choose (at least) 12 datasets of similar paradigm from the WEKA or UCI repositories and at least three algorithms to try out. OR

1b. Choose (at least) 12 algorithms from WEKA you think are appropriate to at least three of your chosen datasets.

• Note that the sledgehammer approach of trying all algorithms on all problems is infeasible/inappropriate.

• Note that some learning algorithms have parameters or variants, try at least three variant algorithms (within the 12).

• Note that some datasets have many attributes, and it may be appropriate to explore attribute selection (additional to the 12).

• Note that some datasets have only numeric data, some nominal (symbolic) data and some mixed data.

• Note that some datasets are for classification tasks and some are for regression tasks.

• When selecting a large group of datasets to test algorithms, try to keep same domain or character.

• When selecting a large group of algorithms to test across datasets, try to keep same broad paradigm.

• Hint. There are many papers describing experiments like this with WEKA or UCI datasets.

• Hint. Section 3 of the text book has tutorials we will go through as we explore WEKA. Hint. WEKA already groups both algorithms and datasets. Although algorithms and datasets are nominally thou

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