Top 36 How To Convert Numeric To Nominal In Weka The 187 Top Answers

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first open the arff file. under filter click “choose” . under filter tree , navigate to unsupervised and search for “NumerictoNomial” . Click once and press Apply.The Nominal to Numerical operator is used for changing the type of non-numeric attributes to a numeric type. This operator not only changes the type of selected attributes but it also maps all values of these attributes to numeric values. Binary attribute values are mapped to 0 and 1.

What is numeric to nominal?

The Nominal to Numerical operator is used for changing the type of non-numeric attributes to a numeric type. This operator not only changes the type of selected attributes but it also maps all values of these attributes to numeric values. Binary attribute values are mapped to 0 and 1.

How do you discretize attributes in Weka?

How do you normalize in Weka?

How can we preprocess dataset in Weka?

weka→filters→supervised→attribute→AttributeSelection

You will notice that it removes the temperature and humidity attributes from the database. After you are satisfied with the preprocessing of your data, save the data by clicking the Save … button. You will use this saved file for model building.

Can numerical data be nominal?

Characteristics of Nominal Data

Nominal data can be both qualitative and quantitative. However, the quantitative labels lack a numerical value or relationship (e.g., identification number). On the other hand, various types of qualitative data can be represented in nominal form.

Can nominal variables be numeric?

Nominal variables are sometimes numeric but do not possess numerical characteristics. Some of thee numeric nominal variables are; phone numbers, student numbers, etc. Therefore, a nominal variable can be classified as either numeric or not.

How do you find a nominal number?

Cardinal numbers, known as the “counting numbers,” indicate quantity. Ordinal numbers indicate the order or rank of things in a set (e.g., sixth in line; fourth place). Nominal numbers name or identify something (e.g., a zip code or a player on a team.) They do not show quantity or rank.

What is attribute discretization?

creating a manageable number of groups of attribute values that. are separated by distinct boundaries. In this way, discretization (or. “bucketization”) allows us to group contiguous values into sets of discrete. values.

What is ordinal attribute?

An ordinal attribute is an attribute whose possible values have a meaningful order or ranking among them, but the magnitude between successive values is not known.

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).

How do you convert nominal data to numeric in Excel?

How do you convert nominal attributes to numeric in Python?

What are nominal data?

Nominal data is data that can be labelled or classified into mutually exclusive categories within a variable. These categories cannot be ordered in a meaningful way. For example, for the nominal variable of preferred mode of transportation, you may have the categories of car, bus, train, tram or bicycle.

How can we replace missing values in Weka?

On explorer, in the preprocess tab, find filter and select choose. Then in the filter, expand filter, then supervised, then attribute. At the bottom, you will find an option “Replace missing value”. Double click on that and then click apply.

How can we remove missing values from dataset in Weka?

How do I convert a CSV file to Arff?

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weka data preprocessing how to convert numeric to nominal

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Weka only changing numeric to nominal – Stack Overflow

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Editing Data In Weka – YouTube

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Nominal to Numerical – RapidMiner Documentation

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How to convert from nominal to numeric data in RapidMiner – Data Mining – YouTube

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NumericToNominal

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Weka wrapper numeric to nominal filter

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How can you discretize numeric attributes?

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Numerical and nominal

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  I read that an algorithm as J48, for example, can work with numerical and nominal attributes. But in the examples of dataset included in the program there are datasets with either nominal or numerical attributes, not both of them in the same dataset. I mean, a column with numerical attributes and a column with nominal attributes and so one, except for the class). So, an algorithm like J48 can classify both types of data together ? And a clustering algorithm? Thank you !
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Weka only changing numeric to nominal

I assume you are using the Weka Explorer (GUI). To apply the filter to specific attributes do the following.

Step 1: Select your filter in the preprocess tab

Step 2: Click on the box to the right of the “Choose” button (a new window opens)

Step 3: In the attributeIndices box enter your custom ranges

If you select the “More” button in the filter window you will get an explanation of the different options and the values you can supply.

In your particular case, the filter is by default applied to the first through last attributes. You should change the range to reflect your personal needs.

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If you are using the Java API, the following code will point you in the right direction.

RapidMiner Documentation

The Nominal to Numerical operator is used for changing the type of non-numeric attributes to a numeric type. This operator not only changes the type of selected attributes but it also maps all values of these attributes to numeric values. Binary attribute values are mapped to 0 and 1. Numeric attributes of input the ExampleSet remain unchanged. This operator provides three modes for conversion from nominal to numeric. This mode is selected by the coding type parameter. Explanation of these coding types is given in the parameters and they are also explained in the example process.

This input port expects an ExampleSet. It is the output of the Retrieve operator in the attached Example Process. The output of other operators can also be used as input. It is essential that meta data should be attached with data for input because attributes are specified in its meta data. The Retrieve operator provides meta data along-with data. The ExampleSet should have at least one non-numeric attribute because if there is no such attribute, the use of this operator does not make sense.

The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.

Tutorial Processes

Nominal to Numeric conversion through different coding types

This Example Process mostly focuses on the coding type and comparison groups parameters. All remaining parameters are mostly for selecting the attributes. The Select Attributes operator also has many similar parameters for the selection of attributes. You can study its Example Process if you want an understanding of these parameters.

The Retrieve operator is used to load the ‘Golf ‘data set. The Nominal to Numerical operator is applied on it. The ‘Outlook’ and ‘Wind’ attributes are selected for this operator for changing them to numeric attributes. Initially, the coding type parameter is set to ‘unique integers’. Thus, the nominal attributes will simply be turned into real valued attributes; the old values will result in equidistant real values. As you can see in the Results Workspace, all occurrences of value ‘sunny’ for the ‘Outlook’ attribute are replaced by 2. Similarly, ‘overcast’ and ‘rain’ are replaced by 1 and 0 respectively. In the same way, all occurrences of ‘false’ value in the ‘Wind’ attribute are replaced by 1 and occurrences of ‘true’ are replaced by 0.

Now, change the coding type parameter to ‘dummy coding’ and run the process again. As dummy coding is selected, for all values of the nominal attribute a new attribute is created. In every example, the new attribute which corresponds to the actual nominal value of that example gets value 1 and all other new attributes get value 0. As you can see in the Results Workspace, ‘Wind=true’ and ‘Wind=false’ attributes are created in place of the ‘Wind’ attribute. In all examples where the ‘Wind’ attribute had value ‘true’, the ‘Wind=true’ attributes gets 1 and ‘Wind=false’ attribute gets 0. Similarly, all examples where the ‘Wind’ attribute had value ‘false’, the ‘Wind=true’ attribute gets value 0 and ‘Wind= false’ attribute gets value 1. The same principle applies to the ‘Outlook’ attribute.

Now, keep the coding type parameter as ‘dummy coding’ and also set the use comparison groups parameter to true. Run the process again. You can see in the comparison groups parameter that ‘sunny’ and ‘true’ are defined as comparison groups for the ‘Outlook’ and ‘Wind’ attributes respectively. As dummy coding is used and the comparison groups are also used thus for all values of the nominal attribute, excluding the comparison group, a new attribute is created. In every example, the new attribute which corresponds to the actual nominal value of that example gets value 1 and all other new attributes get value 0. If the value of the nominal attribute of this example corresponds to the comparison group, all new attributes are set to 0. This is why ‘Outlook=rain’ and ‘Outlook=overcast’ attributes are created but ‘Outlook=sunny’ attribute is not created this time. In examples where the ‘Outlook’ attribute had value ‘sunny’, all new Outlook attributes get value 0. You can see this in the Results Workspace. The same rule is applied on the ‘Wind’ attribute.

Now, change the coding type parameter to ‘effect coding’ and run the process again. You can see in the comparison groups parameter that ‘sunny’ and ‘true’ are defined as comparison groups for the ‘Outlook’ and ‘Wind’ attributes respectively. As effect coding is selected thus for all values of the nominal attribute, excluding the comparison group, a new attribute is created. In every example, the new attribute which corresponds to the actual nominal value of that example gets value 1 and all other new attributes get value 0. If the value of the nominal attribute of this example corresponds to the comparison group, all new attributes are set to -1. This is why ‘Outlook=rain’ and ‘Outlook = overcast’ attributes are created but an ‘Outlook=sunny’ attribute is not created this time. In examples where the ‘Outlook’ attribute had value ‘sunny’, all new Outlook attributes get value -1. You can see this in the Results Workspace. The same rule is applied on the ‘Wind’ attribute.

How to Transform Your Machine Learning Data in Weka

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Last Updated on December 13, 2019

Often your raw data for machine learning is not in an ideal form for modeling.

You need to prepare or reshape it to meet the expectations of different machine learning algorithms.

In this post you will discover two techniques that you can use to transform your machine learning data ready for modeling.

After reading this post you will know:

How to convert a real valued attribute into a discrete distribution called discretization.

How to convert a discrete attribute into multiple real values called dummy variables.

When to discretize or create dummy variables from your data.

Kick-start your project with my new book Machine Learning Mastery With Weka, including step-by-step tutorials and clear screenshots for all examples.

Let’s get started.

Update Mar/2018: Added alternate link to download the dataset as the original appears to have been taken down.

Discretize Numerical Attributes

Some machine learning algorithms prefer or find it easier to work with discrete attributes.

For example, decision tree algorithms can choose split points in real valued attributes, but are much cleaner when split points are chosen between bins or predefined groups in the real-valued attributes.

Discrete attributes are those that describe a category, called nominal attributes. Those attributes that describe a category that where there is a meaning in the order for the categories are called ordinal attributes. The process of converting a real-valued attribute into an ordinal attribute or bins is called discretization.

You can discretize your real valued attributes in Weka using the Discretize filter.

The tutorial below demonstrates how to use the Discretize filter. The Pima Indians onset of diabetes dataset is used to demonstrate this filter because of the input values are real-valued and grouping them into bins may make sense.

You can learn more about the dataset here:

You can also access the dataset directory in your installation of Weka under the data/ directory by loading the file diabetes.arff.

1. Open the Weka Explorer.

2. Load the Pima Indians onset of diabetes dataset.

3. Click the “Choose” button for the Filter and select Discretize, it is under unsupervised.attribute.Discretize.

4. Click on the filter to configure it. You can select the indices of the attributes to discretize, the default is to discretize all attributes, which is what we will do in this case. Click the “OK” button.

5. Click the “Apply” button to apply the filter.

You can click on each attribute and review the details in the “Selected attribute” window to confirm that the filter was applied successfully.

Discretizing your real valued attributes is most useful when working with decision tree type algorithms. It is perhaps more useful when you believe that there are natural groupings within the values of given attributes.

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Convert Nominal Attributes to Dummy Variables

Some machine learning algorithms prefer to use real valued inputs and do not support nominal or ordinal attributes.

Nominal attributes can be converted to real values. This is done by creating one new binary attribute for each category. For a given instance that has a category for that value, the binary attribute is set to 1 and the binary attributes for the other categories is set to 0. This process is called creating dummy variables.

You can create dummy binary variables from nominal attributes in Weka using the NominalToBinary filter.

The recipe below demonstrates how to use the NominalToBinary filter. The Contact Lenses dataset is used to demonstrate this filter because the attributes are all nominal and provide plenty of opportunity for creating dummy variables.

You can download the Contact Lenses dataset from the UCI Machine learning repository. You can also access the dataset directory in your installation of Weka under the data/ directory by loading the file contact-lenses.arff.

1. Open the Weka Explorer.

2. Load the Contact Lenses dataset.

3. Click the “Choose” button for the Filter and select NominalToBinary, it is under unsupervised.attribute.NominalToBinary.

4. Click on the filter to configure it. You can select the indices of the attributes to convert to binary values, the default is to convert all attributes. Change it to only the first attribute. Click the “OK” button.

5. Click the “Apply” button to apply the filter.

Reviewing the list of attributes will show that the age attribute has been removed and replaced with three new binary attributes: age=young, age=pre-presbyopic and age=presbyopic.

Creating dummy variables is useful for techniques that do not support nominal input variables like linear regression and logistic regression. It can also prove useful in techniques like k-nearest neighbors and artificial neural networks.

Summary

In this post you discovered how to transform your machine learning data to meet the expectations of different machine learning algorithms.

Specifically, you learned:

How to convert real valued input attributes to nominal attributes called discretization.

How to convert a categorical input variable to multiple binary input attributes called dummy variables.

When to use discretization and dummy variables when modeling data.

Do you have any questions about data transforms or about this post? Ask your questions in the comments and I will do my best to answer them.

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