How do you select attributes in Weka?

A good place to get started exploring feature selection in Weka is in the Weka Explorer.

1. Open the Weka GUI Chooser.
2. Click the “Explorer” button to launch the Explorer.
3. Open the Pima Indians dataset.
4. Click the “Select attributes” tab to access the feature selection methods.

How many attributes can we see in Weka?

Notice that there are 17 attributes.

How do you discretize attributes in Weka?

Discretize Numerical Attributes

1. Open the Weka Explorer.
2. Load the Pima Indians onset of diabetes dataset. Weka Explorer Loaded Diabetes Dataset.
3. Click the “Choose” button for the Filter and select Discretize, it is under unsupervised.
4. Click on the filter to configure it.
5. Click the “Apply” button to apply the filter.

What are attributes in Weka?

Once an attribute has been created, it can’t be changed. This type of attribute represents a dynamically expanding set of nominal values. String attributes are not used by the learning schemes in Weka. They can be used, for example, to store an identifier with each instance in a dataset.

What are the different methods for relevant attribute selection?

There are three types of feature selection: Wrapper methods (forward, backward, and stepwise selection), Filter methods (ANOVA, Pearson correlation, variance thresholding), and Embedded methods (Lasso, Ridge, Decision Tree).

What is attribute subset selection in data mining?

The goal of attribute subset selection is to find a minimum set of attributes such that dropping of those irrelevant attributes does not much affect the utility of data and the cost of data analysis could be reduced. Mining on a reduced data set also makes the discovered pattern easier to understand.

How can I check my data in Weka?

Open The Weka GUI Chooser. Click “Explorer” to open the Weka Explorer….It lists a lot of information about the selected attribute, such as:

1. The name of the attribute.
2. The number of missing values and the ratio of missing values across the whole dataset.
3. The number of distinct values.
4. The data type.

How do you convert nominal attribute to numeric in Weka?

There is no direct filter to convert nominal data to numeric data. If your nominal attribute has 2 values (SEX: MALE, FEMALE) you can easily apply the filter under unsupervised filters “nominal to binary”. But if you have more than 2 variations for the attribute, you cannot use “nominal to binary”.

Why might we want to discretize an attribute?

Discretizing is transforming numeric attributes to nominal. You might want to do that in order to use a classification method that can’t handle numeric attributes (unlikely), or to produce better results (likely), or to produce a more comprehensible model such as a simpler decision tree (very likely).

What is class attribute in data mining?

To predict a classification, you must specify the input fields to be classified and the class label field. The class label field contains the class labels of the classes to which the records in the source data were attributed during the historical classification. …

How are nominal attributes defined in .arff file Mcq?

Nominal attributes Nominal values are defined by providing an listing the possible values: {, , .} For example, the class value of the Iris dataset can be defined as follows: @ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica}

How do I perform a logistic regression in Weka?

The logistic regression only supports binary classification problems, although the Weka implementation has been adapted to support multi-class classification problems. Choose the logistic regression algorithm: Click the “Choose” button and select “Logistic” under the “functions” group.

What file formats does Weka support?

In addition to the native ARFF data file format, WEKA has the capability to read in “.csv” format files. This is fortunate since many databases or spreadsheet applications can save or export data into flat files in this format.

How does the Weka discretization filter work?

We will rely on WEKA to perform discretization on the “age” and “income” attributes. In this example, we divide each of these into 3 bins (intervals). The WEKA discretization filter, can divide the ranges blindly, or used various statistical techniques to automatically determine the best way of partitioning the data.

How many input variables are there in the Weka dataset?

There are 34 numerical input variables of generally the same scale. You can learn more about this dataset on the UCI Machine Learning Repository. Top results are in the order of 98\% accuracy. Start the Weka Explorer: Open the Weka GUI Chooser. Click the “Explorer” button to open the Weka Explorer.