I'm trying to use ID3 algorithm in WEKA. I installed package. But id3 isn't activated.
ID3 isn't activated
How to activate this? and also how to activate OneR rule?
oneR isn't activated.
ID3 only handles categorical attributes and class attribute. From the More dialog in the GenericObjectEditor, you can see the following capabilities:
Class - Nominal class, Binary class, Missing class values
Attributes - Binary attributes, Unary attributes, Nominal attributes, Empty nominal attributes
OneR can handle categorical and numeric attributes, but only a categorical class attribute. The capabilities from the More dialog:
Class - Nominal class, Binary class, Missing class values
Attributes - Binary attributes, Unary attributes, Numeric attributes, Nominal attributes, Date attributes, Empty nominal attributes, Missing values
Without knowing what your dataset looks like, I can only assume that you might have a class attribute that is of type STRING or NUMERIC.
If you want to use a certain algorithm, you have to preprocess your data accordingly.
Related
I was doing a machine learning task in Weka and the dataset has 486 attributes. So, I wanted to do attribute selection using chi-square and it provides me ranked attributes like below:
Now, I also have a testing dataset and I have to make it compatible. But how can I reorder the test attributes in the same manner that can be compatible with the train set?
Changing the order of attributes (e.g., when using the Ranker in conjunction with an attribute evaluator) will probably not have much influence on the performance of your classifier model (since all the attributes will stay in the dataset). Removing attributes, on the other hand, will more likely have an impact (for that, use subset evaluators).
If you want the ordering to get applied to the test set as well, then simply define your attribute selection search and evaluation schemes in the AttributeSelectedClassifier meta-classifier, instead of using the Attribute selection panel (that panel is more for exploration).
I want to calculate confusion matrix, f1 score, roc etc. But the Weka output is showing this. How can I get the confusion matrix, f1 score, roc, etc?
First of all, your dataset seems to have a numeric class attribute. Correlation coefficient is a statistic generated for regression models. A confusion matrix (which you want) is only computed for classification models.
Secondly, you are using ZeroR as classifier, which is not a very useful classifier (only for determining a baseline). ZeroR either predicts the mean class value (numeric class attribute) or the majority class (nominal class attribute).
Solutions:
Ensure that you are using the right attribute for your class. Assuming that you are using the Weka Explorer, check the combobox on the Classify panel that it has the right attribute selected. On the command-line, use the -c flag to specify the index of the class attribute (1-based index, first and last can be used as well).
If you imported your data from a CSV file and the class attribute column contains only numeric values, then Weka will have left it as numeric (it doesn't know that this column represents a nominal attribute). In that case, make sure that you convert your class attribute to a nominal one, e.g., by using the NumericToNominal filter in the Preprocess panel.
Choose a different classifier, like RandomForest or J48, which tend to generate reasonable models with just the default parameters.
I need to do feature selection using information gain in learning to rank. I try to use Weka to implement this. But I found in "Select attributes"-"Attribute Evaluator", the "InfoGainAttributeEval" is not available to use. I do not know how to install it and make it available. Anybody knows how to fix this problem?
This usually means that something about your dataset is not compatible with the technique you are trying to use.
Although the InfoGainAttributeEval entry is greyed out in the list, you should still be able to select it (note the Start button is now greyed out), click on its name and then click Capabilities which should show you:
CAPABILITIES
Class -- Binary class, Missing class values, Nominal
class
Attributes -- Binary attributes, Date attributes, Empty nominal
attributes, Missing values, Nominal attributes, Numeric attributes,
Unary attributes
Does your data have attributes that don't match these requirements, or have you selected a class attribute that doesn't match the class requirements?
I cant seem to find out what attribute selection filter does in pre process tab? someone could please tell me in simple language as im new to weka
when i apply it to my dataset it seems to remove a couple of attributes but im unsure why
A real data set may contain many attributes. Applying any data mining process on this data set (e.g. finding clusters, generating a classification model ...) may take very long time.
Instead of that, we can select some attributes(dimensions) which is called the most discriminative attributes. These attributes can almost describe the data set with lower number of attributes and this will speed up any process done on the data.
Attribute selection tab contains many different methods for selecting these attributes. One of them is CFS Feature Set Evaluation This filter gives you the attributes that have higher correlation with the class label which makes them discriminative attributes.
I am new to weka.. My data contains a column of student name. I want to convert these names to numeric values, over the whole column.
Eg: Suppose there are 10 names abcd ,cdef,xyz ,etc. I want to pre process the data so that corresponding to each name there is distinct numeric value, like abcd changes to 1 ,cdef changes to 2 ,etc.
Also two or more rows can have same name. So in this case, same name should have same value.
Please help me...
Weka supports 4 non-relational attribute types: nominal, numeric, string and date. You can find out more about them in Weka Manual (it can be found in the same folder were you downloaded Weka), chapter "The ARFF Header Section".
You should find out what is the type of the "student's name" attribute (probably string, but could be nominal), and decide what should be the type of the attribute with converted values (numeric, nominal, or string).
There can be 2 scenarios:
(1) If types of the existing and desired attributes are the same (string-string or nominal-nominal, i.e. you only want to change values, not attribute type), you could do so
(a) manually - open the data file in Weka Explorer, and click Edit... button, or
(b) write a small program using Weka's Attribute class functions value and setValue.
(2) Types are different - Weka attribute types cannot be converted, so you will have to create and insert a new attribute with the converted values, and delete the old attribute. An example of how to create a new attribute can be found at
http://weka.wikispaces.com/Programmatic+Use#Step.
As far as I understand, strictly converting names into a "numeric" type doesn't seem like the best approach, within the context of WEKA - WEKA will treat numeric attributes differently than it does "string" or "nominal" attributes (for example, for running certain "attribute selection" algorithms, you can not use "numeric" types - they need to be "discretized" or converted into nominal form).
So, for your case, I think you can convert your "string" names into just "nominal" type using the StringToNominal class (this class acts as a WEKA "filter" to help convert a given "string" attribute into an attribute of type "nominal"). This will also take care about the repeating names - the list of "nominal" values for the names (that will be generated after you apply this filter) will contain any given name (that appears any number of times) only one time.
"Nominal" attributes also have the advantage that implicitly, they do have a numeric representation (the index of the value within the set of values; similar to how the "enums" in Java have a numeric index). So, you can utilize that as the "numeric" information corresponding to the names (though as I said earlier, it's probably best to just use it as "nominal" attribute; really depends on your particular use case).
I had the same problem as the one mentioned in the question, and I could "address" it in the following way.
I first applied the StringToNominal filter as mentioned before (don't forget to change the attribute range (from "last" to "first-last")). Once done that, I saved the dataset in LibSVM format, which changes the nominal values to numeric ones.
Then, if you close Weka and open it again, you will have the same dataset with the same number of features but they will be numeric. Now some changes should be done, first of all, normalizing all the numeric values in the dataset, using the Normalize filter. After that, apply the NumericToNominal filter to the last attribute.
Then, you will have a similar dataset with numeric values.
Hope this helps.