9
votes
Accepted
How do i calculate prediction probability of a class in Java Weka Api?
Welcome to the community!
You can replace your code by the following code.
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8
votes
Accepted
How to use SMOTE in Java Weka API?
Welcome to the community.
You can use the following code:
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- 898
7
votes
Accepted
How do I use a custom stopwords filter in the Java Weka API?
You can try the following code.
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7
votes
How do I use a custom stopwords filter in the Java Weka API?
First of all you have to prepare a text file for your custom stopwords. Then you can use the following code:
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5
votes
Accepted
Official page of Weka for SVM java code
The official Weka source code is stored in a local Gitlab git repository (not on Github).
Note that there are two versions of SVM commonly used with Weka:
The SMO classifier, source code here.
The ...
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4
votes
What can weka do that python and sklearn can't?
Weka's decision trees are from the Quinlan family, whereas sklearn uses CART.
The most notable difference is that Quinlan trees aren't restricted to binary splits: a categorical column will be split ...
- 12.3k
3
votes
Accepted
Converting a nominal attributes to numerical ones in data set
By converting a nominal attribute to a single numeric attribute as you described, you are implicitly introducing an ordering over the nominal labels which is a bad representation of the data, and can ...
- 3,950
3
votes
Which feature selection I should trust?
It is generally not best practice to trust ML techniques to do the work for you. A data scientist knowns on what principles the methods that he or she applies work, and then stills tries multiple in ...
- 1,364
3
votes
Accepted
is there any way to plot ROC curves from weka
In the Weka explorer, go to the classify tab and train/test your algorithm.
The result buffer appears in the bottom left box under the section labeled "result list"
Right click the result buffer and ...
- 2,632
3
votes
Accepted
Weka: Implementation of Random Forest
Your linked paper appears to be wrong about feature subsetting. I couldn't find it in the documentation for randomForest, but the source for randomForest uses randomTree for the base models, and in ...
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2
votes
Which feature selection I should trust?
I would look at Sebastian Raschka's discussion on different types of feature selection methods.. In short, there appear to be three categories (each with advantages and disadvantages):
Filters
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2
votes
different results with MEKA vs Scikit-learn!
According to this question
LinearSVC is not linear SVM, do not use it if do not have to.
and furthermore
LinearSVC [is] one of the mistakes of sklearn developers.
In this picture from the ...
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2
votes
Converting a nominal attributes to numerical ones in data set
For encoding of the categorical variables with high cardinality (i.e. with large number of levels) you may want to try the so called impact coding.
The main idea is very simple, you just split the ...
- 1,520
2
votes
Accepted
Is there t-SNE in WEKA?
Sadly no, there is not a T-SNE implementation for WEKA.
If you can install python packages in your environment, then you can use the wekaPython package (in WEKA's ...
- 3,950
2
votes
Setting attribute weights in Weka
Yes. You can add weights to the dataset's attributes.
To first add the weights:
Open the dataset in explorer
Perform any required filtering (if necessary)
Click the "Edit" button on the top panel
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2
votes
What is the best option to test the data set of images using weka?
Data Preparation
Weka uses the arff file type.
A modified example arff file content from the previous link:
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2
votes
Optimum minimum number of instances in weka's j48
If there were an optimum in terms of number of instances or attributes, then that would be set in the program. No, such a parameter generally has an optimum that depends on the data itself, and needs ...
- 12.3k
2
votes
The Differences Between Weka Random Forest and Scikit-Learn Random Forest
For the question -
Why the performances of weka random forest and sklearn random forest
are similar but they use different methods to compute class
probabilities of an input instance?
Often ...
- 1,014
2
votes
Is my model overfitting? Weka Random Forest
No, this is not a sign of overfitting. The two measures (accuracy and ROC area under the curve) are not comparable.
In order to check for overfitting you need to apply the trained model on the ...
- 25.9k
2
votes
Accepted
Select the best feature selection method for classification
I don't think that there is a single feature selection method that works best with a specific algorithm, what they do is selecting the best features based on various criteria. These features can be ...
- 636
2
votes
Accepted
Correct way to take a subset of a dataset?
Do I need to carefully preserve the information contained in the 100,000 rows or can I cherry pick whatever 1000 rows I want?
The concern about Class Imbalance you're expressing is: "Will my
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- 1,180
1
vote
Accepted
Where are WEKA installed packages stored
I don't know how WEKA stores the extra packages on other installations, but for my Windows 10 with WEKA 3.8 it was here:
WEKA stores the extra packages in a direcory called packages. This package was ...
- 165
1
vote
What does random seed value mean in Weka?
Seed is just a value by which you can fix the Random Numbers that are being generated in your task. Also, this is a general concept and not just for weka.
So, here random numbers are being used to ...
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1
vote
Accepted
opening Weka from command line
I think your manual is for an older version of Weka. I have version 3.9.3. In some version the Weka developers have perhaps changed some internal structures and class names.
In a terminal window ...
- 612
1
vote
Parameters optimization algorithms in Weka
Supposing that you refer to the performance of tuning algorithms as the improvement in algorithm performance (accuracy, error, etc.). The effectiveness of parameter optimization (or tuning) is ...
- 300
1
vote
Accepted
Repeated training and testing in Weka?
In general the advantage of repeated training/testing is to measure to what extent the performance is due to chance. The most common source of chance comes from which instances are selected as ...
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1
vote
SMOTE on training data
Even I'm also not sure about Weka, but so far I read, it provides functionality for data mining, data pre-processing, model training.
Generally what I do,
In case of pre-processing: model generated ...
- 1,282
1
vote
What does Make Density Based Clusterer in Weka do?
Based on this paper, MakeDensityBasedClusterer is a metaclusterer that wraps a clustering algorithm to make it return a probability distribution and ...
- 111
1
vote
Multivariate analysis
Depending on the number of dependent variables (when they are categorical) you could also consider doing the following. Imagine we have images of cats and dogs which are happy and sad.
We could ...
- 1,958
1
vote
Multivariate analysis
No. I don't think so. And I don't think that is what "multivariate" means.
I rather think all of Weka's APIs and classifiers assume that there is exactly one attribute which gets assigned the "...
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