I am trying to run the "Voted Perceptron" on iris data set using weka. However, when I load the data, the Voted Perceptron refuses to run, while it runs on many other data sets like ionosphere.arff, diabetes.arff etc.
Please help.
Because VotedPerceptron only works on datasets that are binary classifiers. Iris.arff has three different classification types while diabetes.arff and ionosphere.arff only have two.
If you want it to work you'll have to entirely remove one of iris.arff's classification types.
Related
I am fairly new to machine learning and I am trying to use WEKA (GUI) to implement a neural network on a sports data set. My issue is that I want my inputs to be Arrays (each Array is a contestant with stats such as speed, winrate, etc). I am wondering how I can tell WEKA that each input is an array of values.
You can define it in an .arff file. See this website for detailed information. As the figure below.
Or after opening your data in Weka, you can convert it with the help of some filters. I do not know the current format of your data. However, if you can open it in Weka, you can edit your data with many filters. Meanwhile, artificial neural networks only accept numerical values. Among these filters, there are those who convert nominal data to numerical data. I share an image from these filters below. If you are new to this area, I recommend you to watch videos of WekaMOOC (owned by Weka developers.). I think it will be very useful. Good luck.
Weka_filters_screen
I have two datasets regarding whether a sentence contains a mention of a drug adverse event or not, both the training and test set have only two fields the text and the labels{Adverse Event, No Adverse Event} I have used weka with the stringtoWordVector filter to build a model using Random Forest on the training set.
I want to test the model built with removing the class labels from the test data set, applying the StringToWordVector filter on it and testing the model with it. When I try to do that it gives me the error saying training and test set not compatible probably because the filter identifies a different set of attributes for the test dataset. How do I fix this and output the predictions for the test set.
The easiest way to do this for a one off test is not to pre-filter the training set, but to use Weka's FilteredClassifier and configure it with the StringToWordVector filter, and your chosen classifier to do the classification. This is explained well in this video from the More Data Mining with Weka online course.
For a more general solution, if you want to build the model once then evaluate it on different test sets in future, you need to use InputMappedClassifier:
Wrapper classifier that addresses incompatible training and test data
by building a mapping between the training data that a classifier has
been built with and the incoming test instances' structure. Model
attributes that are not found in the incoming instances receive
missing values, so do incoming nominal attribute values that the
classifier has not seen before. A new classifier can be trained or an
existing one loaded from a file.
Weka requires a label even for the test data. It uses the labels or „ground truth“ of the test data to compare the result of the model against it and measure the model performance. How would you tell whether a model is performing well, if you don‘t know whether its predictions are right or wrong. Thus, the test data needs to have the very same structure as the training data in WEKA, including the labels. No worries, the labels are not used to help the model with its predictions.
The best way to go is to select cross validation (e.g. 10 fold cross validation) which automatically will split your data into 10 parts, using 9 for training and the remaining 1 for testing. This procedure is repeated 10 times so that each of the 10 parts has once been used as test data. The final performance verdict will be an average of all 10 rounds. Cross validation gives you a quite realistic estimate of the model performance on new, unseen data.
What you were trying to do, namely using the exact same data for training and testing is a bad idea, because the measured performance you end up with is way too optimistic. This means, you‘ll get very impressive figures like 98% accuracy during testing - but as soon as you use the model against new unseen data your accuracy might drop to a much worse level.
I am classifying iris data using DECISION TREE (C4.5), RANDOM FOREST and NAIVE BAYES. I am using the dataset downloaded from iris-train and iris-test. When I train the all networks everything is fine with proper results with 'classifier output', 'Detailed accuracy with class' and 'confusion matrix'. But, when I select the iris-test data in the Weka-explorer-classify-test options and select the iris-test file and in 'more options' select 'output prediction' as 'csv' and click start, I am getting the result as shown in the figure below. The 'classifier output' is showing the classified samples correctly, but, 'Detailed accuracy with class' and 'confusion matrix' is with all values zeros. Any suggestion where I am going wrong in selecting any option. Thank you.
The confusion matrix shows you how well your trained classifier performs by comparing the actual class of the instances in the test set with the class that was predicted by the classifier. But you are supplying a test set with no class information, so there's nothing to compare against. This is why you see
Total Number of Instances 0
Ignored Class Unknown Instances 120
in the output in your screenshot.
Typically you would first evaluate the performance of your classifier using cross-validation, or a test set that has class information. Then you can use the trained classifier to classify unknown data, for example using the Re-evaluate model on current test set right-click option as described in the help.
I am trying to learn Weka.I am using a data set which has three classes of activity. I am trying to build a classifier, use ten-fold cross validation and tabulate the accuracy. However i cant tell which data belongs to which class. How do i proceed? I am not sure how to upload the data set here.Any help would be appreciated.
In order to get results using a k-fold cross validation, your data points must have class labels. For instance, if I give you a set of data and ask you to classify them into three classes but I do not know the classes of the data points, when you classify them and return them back to me, how do I calculate your classification accuracy?
i'm using weka to do some text mining, i'm a little bit confused so i'm here to ask how can i ( with a set of comments that are in a some way classified as: notes, status of work, not conformity, warning) predict if a new comment belong to a specific class, with all the comment (9551) i've done a preprocess obtaining with the filter "stringtowordvector" a vector of tokens, and then i've used the simple kmeans to obtain a number of cluster.
So the question is: if a user post a new comment can i predict with those data if it belong to a category of comment?
sorry if my question is a little bit confused but so am i.
thank you
Trivial Training-validation-test
Create two datasets from your labelled instances. One will be training set and the other will be validation set. The training set will contain about 60% of the labelled data and the validation will contain 40% of the labelled data. There is no hard and fast rule for this split, but a 60-40 split is a good choice.
Use K-means (or any other clustering algorithm) on your training data. Develop a model. Record the model's error on training set. If the error is low and acceptable, you are fine. Save the model.
For now, your validation set will be your test dataset. Apply the model you saved on your validation set. Record the error. What is the difference between training error and validation error? If they both are low, the model's generalization is "seemingly" good.
Prepare a test dataset where you have all the features of your training and test dataset but the class/cluster is unknown.
Apply the model on the test data.
10-fold cross validation
Use all of your labelled data instances for this task.
Apply K-means (or any other algorithm of your choice) with a 10-fold CV setup.
Record the training error and CV error. Are they low? Is the difference between the errors is low? If yes, then save the model and apply it on the test data whose class/cluster is unknown.
NB: The training/test/validation errors and their differences will give you an "very initial" idea of overfitting/underfitting of your model. They are sanity tests. You need to perform other tests like learning curves to see if your model overfits or underfits or perfect. If there appears to be an overfitting and underfitting problem, you need to try many different techniques to overcome them.