• Download iris dataset (iris.arff) and save it in a local folder. • Load the dataset into the Explorer and start analyzing it. In order to do this, click the Open file button to bring up a standard dialog through which you can select a file. • Choose iris.arff from your local folder • You’re now in the Preprocess panel. Answer the following questions: 1. What information do you have about the dataset (e.g., number of instances, attributes and classes)? What type of attributes does this dataset contain (nominal or numeric?) what are the classes in this dataset? Which attribute has the greatest standard deviation? What Does it tell you about that attribute? You might also find it useful to open iris dataset in your favorite text editor or click the Edit button from the row of buttons at the top of the Preprocess panel. 2. Under Filter choose the Standardize filter and apply it to all attributes. What does it do? How does it affect the attributes’ statistics? Click Undo and now apply the Normalize filter and apply it to all the attributes. What does it do? How does it affect the attributes’ statistics? How does it differ from Standardize? Click Undo again to return the data to its original state. 3. Give definitions and examples of the two following graphical representations: a. Scatter plot b. Histogram 4. At the bottom right of the window there should be a graph that visualizes the dataset, making sure Class:class (Nom) is selected in the drop down box. Click Visualize All. What can you interpret from these graphs? Which attribute(s) discriminate best between the classes in the dataset? How do the Standardize and Normalize filters affect these graphs?
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