classification

I have a dataset that has a unique identifier and other features. It looks like this ID LenA TypeA LenB TypeB Diff Score Response 123-456 51 M 101 L 50 0.2 0 234-567 46 S 49 S 3 0.9 1 345-678 87 M 70 M 17 0.7 0 I split it up into training and test data. I am trying to classify test data into two classes from a classifier trained on training data. I want the identifier in the training and testing dataset so I can map the predictions back to the IDs . Is there a way that I can assign the identifier column as a ID or non-predictor like we can do in Azure ML Studio or SAS? I am using the

I would like to do some tests with neural network final hidden activation layer outputs using sklearn's MLPClassifier after fit ting the data. for example, If I create a classifier, assuming data X_train with labels y_train and two hidden layers of sizes (300,100) clf = MLPClassifier(hidden_layer_sizes=(300,100)) clf.fit(X_train,y_train) I would like to be able to call a function somehow to retrieve the final hidden activation layer vector of length 100 for use in additional tests. Assuming a test set X_test, y_test , normal prediction would be: preds = clf.predict(X_test) But, I would like to

First thanks for reading me and thanks a lot if you can give any clue to help me solving this. As I'm new to Scikit-learn, don't hesitate to provide any advice that can help me to improve the process and make it more professional. My goal is to classify data between two categories. I would like to find a solution that would give me the most precise result. At the moment, I'm still looking for the most suitable algorithm and data preprocessing. In my data I have 24 values : 13 are nominal, 6 are binarized and the others are continuous. Here is an example of a line "RENAULT";"CLIO III";"CLIO III

This is my first post here so I hope to be able to ask my question properly :-) I want to do "elephant detection" by classifying color samples (I was inspired by this paper ). This is the pipeline of my "solution" until the training of the classifier: Loading a set of 4 training images (all containing an elephant), and then splitting them in two images: one containing the environment surrounding the elephant (the "background"), and one containing the elephant (the "foreground"); Mean shift segmentation of the backgrounds and the foregrounds; RGB -> Luv color space conversion and pixel values

I need a library for naïve Bayes large scale, with millions of training examples and +100k binary features. It must be an online version (updatable after training). I also need top-k output, that is multiple classifications for a single instance. Accuracy is not very important. The purpose is an automatic text categorization application. Any suggestions for a good library is very appreciated. EDIT: The library should preferably be in Java. If a learning algorithm other than naïve Bayes is also acceptable, then check out Vowpal Wabbit (C++), which has the reputation of being one of the best