I am using Weka Java API for training a model and making predictions. I am able to build a classifier based on 3 algorithms: Decision Trees, Naïve Bayes and Random Forest. Then able to classify a test instance and get a probability distribution over the target classes.
My question is – how do I show the reasoning/basis [consumable result, easily understandable] for the prediction. Why a given instance was classified as ‘A’, ‘B’ or ‘C’. Since the end-user would also like to know the logic behind the classification.
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Is it possible in Weka to train a model minimizing a cost factor?
I have a data set containing a cost factor in each sample. It defines what using this sample would cost. Now, I would like to select as much of the samples as possible while minimizing this cost factor.
E.g. with Multilayer perceptron, I want to train the neurons in a way, that it chooses as many samples as possible while minimizing the sum of the cost factor.
I've checked all the model options and also searched the package manager for something like that, but I was unable to find anything. Could someone tell me whether this can be done using Weka?
What you are describing sounds more like an optimization problem rather than a classification or regression problem (for which you would use a Weka classifier).
Weka does have some limited support for optimization through its abstract weka.core.Optimization class (e.g., used internally by weka.classifiers.functions.Logistic). But that requires implementing some methods.
To cast your net wider, you might want to take a look at the following article that describes various optimization techniques:
https://machinelearningmastery.com/tour-of-optimization-algorithms/
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 have used the output predictions of J48 classifier in Weka and got the results with predictions (probability). As I need to use these predictions number in my research, I need to know how the weka calculates these numbers? What is the formula? Is it specified for each classifier?
In addition to Jan Eglinger answer.
The J48 classifier is Weka's implementation of the infamous C4.5 decision tree classifier, which is a classification algorithm based on ID3 that classifies using information entropy.
The training data is a set S = {s_1, s_2, ...} of already classified samples. Each sample s_i consists of a p-dimensional vector (x_{1,i}, x_{2,i}, ...,x_{p,i}) , where the x_j represent attribute values or features of the sample, as well as the class in which s_i falls.
At each node of the tree, C4.5 chooses the attribute of the data that most effectively splits its set of samples into subsets enriched in one class or the other. The splitting criterion is the normalized information gain (difference in entropy). The attribute with the highest normalized information gain is chosen to make the decision. The C4.5 algorithm then recurs on the smaller sublists.
This algorithm has a few base cases.
All the samples in the list belong to the same class. When this
happens, it simply creates a leaf node for the decision tree saying
to choose that class.
None of the features provide any information gain. In this case,
C4.5 creates a decision node higher up the tree using the expected
value of the class.
Instance of previously-unseen class encountered. Again, C4.5 creates
a decision node higher up the tree using the expected value.
You can find the information Gain and entropy in the Weka Api package. For that you need to start dubbing the java weka api and go through each step.
In general, if you don't worry about how algorithm works internally using high level mathematics. Try to calculate InformationGain and entropy and explain them in your research apart from decision trees, you have methods for both of these to calculate their value.
What is the formula?
Weka's J48 classifier is an implementation of the C4.5 algorithm.
I need to know how the weka calculates these numbers?
You can find implementation details in J48.java and in the weka.classifiers.trees.j48 package.
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.
I'm using Weka and would like to perform regression with random forests. Specifically, I have a dataset:
Feature1,Feature2,...,FeatureN,Class
1.0,X,...,1.4,Good
1.2,Y,...,1.5,Good
1.2,F,...,1.6,Bad
1.1,R,...,1.5,Great
0.9,J,...,1.1,Horrible
0.5,K,...,1.5,Terrific
.
.
.
Rather than learning to predict the most likely class, I want to learn the probability distribution over the classes for a given feature vector. My intuition is that using just the RandomForest model in Weka would not be appropriate, since it would be attempting to minimize its absolute error (maximum likelihood) rather than its squared error (conditional probability distribution). Is that intuition right? Is there a better model to be using if I want to perform regression rather than classification?
Edit: I'm actually thinking now that in fact it may not be a problem. Presumably, classifiers are learning the conditional probability P(Class | Feature1,...,FeatureN) and the resulting classification is just finding the c in Class that maximizes that probability distribution. Therefore, a RandomForest classifier should be able to give me the conditional probability distribution. I just had to think about it some more. If that's wrong, please correct me.
If you want to predict the probabilities for each class explicitly, you need different input data. That is, you would need to replace the value to predict. Instead of one data set with the class label, you would need n data sets (for n different labels) with aggregated data for each unique feature vector. Your data would look something like
Feature1,...,Good
1.0,...,0.5
0.3,...,1.0
and
Feature1,...,Bad
1.0,...,0.8
0.3,...,0.1
and so on. You would need to learn one model for each class and run them separately on any data to be classified. That is, for each label you learn a model to predict a number that is the probability of being in that class, given a feature vector.
If you don't need the probabilities to be predicted explicitly, have a look at the Bayesian classifiers in Weka, which make use of probabilities in the models that they learn.