classification

I have searched the net for the all the possible values that you can put in the scope tag inside dependency tag, but I haven't found any list with the same data for the classiffier and the type. Anybody knows what I can and cannot put inside this tags? Just to be clear, I am not asking what does the classifier tag or the type tag do, I just want a list of the data that this tag accepts or where can I find it. Thanks! From the Maven Reference : Update Oops, I misunderstood the question. Here is my second shot (important parts italicized by me): The classifier allows to distinguish artifacts

Im working on a binary classification model, classifier is naive bayes. I have an almost balanced dataset however I get the following error message when I predict: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. 'precision', 'predicted', average, warn_for) I'm using gridsearch with CV k-fold 10. The test set and predictions contain both classes, so I don't understand the message. I'm working on the same dataset, train/test split, cv and random seed for 6 other models and those work perfect. Data is ingested externally into

Can you please let me know how to represent attribute or class for text classification in weka. By using what attribute can I do classification? word frequency or just word? What would be possible structure of ARFF format? Can you give me several lines of example of that structure? Thank you very much in advance. One of the easiest alternatives is to start with an ARFF file for a two class problem like: @relation corpus @attribute text string @attribute class {pos,neg} @data 'long text with words ... ',pos The text is represented as a String type and the class is a nominal with two values.

I created an LSTM network for sequence classification (binary) where each sample has 25 timesteps and 4 features. The following is my keras network topology: Above, the activation layer after Dense layer uses softmax function. I used binary_crossentropy for loss function and Adam as the optimizer to compile the keras model. Trained the model with batch_size=256, shuffle=True and validation_split=0.05, The following is the training log: Train on 618196 samples, validate on 32537 samples 2017-09-15 01:23:34.407434: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node

I'm trying to use weka API in my java code. I use J48 tree classification to Classify my dataset in MySQL database, but I have this error: Trying to add database driver (JDBC): RmiJdbc.RJDriver - Error, not in CLASSPATH? Trying to add database driver (JDBC): jdbc.idbDriver - Error, not in CLASSPATH? Trying to add database driver (JDBC): com.mckoi.JDBCDriver - Error, not in CLASSPATH? Trying to add database driver (JDBC): org.hsqldb.jdbcDriver - Error, not in CLASSPATH? weka.core.UnassignedClassException: weka.classifiers.trees.j48.C45PruneableClassifierTree: Class attribute not set! at weka

We are currently attempting to port a very (very) large project built with ant to maven (while also moving to svn). All possibilities are being explored in remodeling the project structure to best fit the maven paradigm. Now to be more specific, I have come across classifiers and would like to know how I could use them to my advantage, while refraining from "classifier anti-patterns". Thanks from: http://maven.apache.org/pom.html classifier: You may occasionally find a fifth element on the coordinate, and that is the classifier. We will visit the classifier later, but for now it suffices to