multilabel-classification

问题 I am working on a multilabel and multiclass classification framework, I want to add matrices for multilabel and multiclass accuracy calculation. Here is demo data : predicted_labels = [[1,0,0,0,1],[1,0,0,0,1],[1,0,0,0,1],[1,0,0,0,1],[1,0,0,0,1],[1,0,1,0,1]] true_labels = [[1,1,0,0,1],[1,0,0,1,1],[1,0,0,0,1],[1,1,1,0,1],[1,0,0,0,1],[1,0,0,0,1]] Most popular accuracy matrices for multi-label, multi-class classification are : Hamming score Hamming loss Subset accuracy The code for the above

问题 I have been stuck with multi-label classification for sometime(I must say I am quite new to Neural Network). First I will explain the network I am trying to train. I have a 1000 classes in the network and they have multi-label outputs. For each training example, the number of positive output is same(i.e 10) but they can be assigned to any of the 1000 classes. So 10 classes have output 1 and rest 990 have output 0. For the multi-label classification, I am using 'binary-cross entropy' as cost

问题 I have a data set with a target variable that can have 7 different labels. Each sample in my training set has only one label for the target variable. For each sample, I want to calculate the probability for each of the target labels. So my prediction would consist of 7 probabilities for each row. On the sklearn website I read about multi-label classification, but this doesn't seem to be what I want. I tried the following code, but this only gives me one classification per sample. from sklearn

问题 I'm wondering how to calculate precision and recall measures for multiclass multilabel classification, i.e. classification where there are more than two labels, and where each instance can have multiple labels? 回答1: For multi-label classification you have two ways to go First consider the following. is the number of examples. is the ground truth label assignment of the example.. is the example. is the predicted labels for the example. Example based The metrics are computed in a per datapoint

问题 I have a dataset of images that have multiple labels; There are 100 classes in the dataset, and each image has 1 to 5 labels associated with them. I'm following the instruction in the following URL: https://github.com/BVLC/caffe/issues/550 It says that I need to generate a text file listing the images and its labels as in /home/my_test_dir/picture-foo.jpg 0 /home/my_test_dir/picture-foo1.jpg 1 In my case, since I have multi-label images, does it work to simply add labels as in following?

问题 I am trying to get a tensorflow network that does multi-label predictions. Using softmax with one-hot (single label) predictions works correctly. The accuracy get's calculated perfectly and the network learns as it should. My basic network setup is: X = tf.placeholder(features.dtype, (None, 300), name="input") y = tf.placeholder(hots.dtype, (None,64), name="labels") with tf.name_scope("dnn"): hidden1 = fully_connected(X, 900, scope="hidden1") hidden2 = fully_connected(hidden1, 450, scope=

问题 I am doing multilabel classification, where I try to predict correct labels for each document and here is my code: mlb = MultiLabelBinarizer() X = dataframe['body'].values y = mlb.fit_transform(dataframe['tag'].values) classifier = Pipeline([ ('vectorizer', CountVectorizer(lowercase=True, stop_words='english', max_df = 0.8, min_df = 10)), ('tfidf', TfidfTransformer()), ('clf', OneVsRestClassifier(LinearSVC()))]) predicted = cross_val_predict(classifier, X, y) When running my code I get

问题 I need to train a model which would perform multilabel multiclass classification on text data. I am presently trying to do the same using mlr package in R , following the directions in this link - Multilabel Classification (using mlr R package) 1) Is there any other package recommended? 2) Otherwise, I am stuck at this place (as instructed in the article mentioned above) classify <- getTaskData(dtmDf) ## dtmDf is my dtm converted to dataframe form 'classify' is NULL Any help/ directions would

问题 Receiving IndexError: index 3 is out of bounds for axis 1 with size 3 when trying to create one-hot encoding using Keras to_categorical on output vectors. Y.shape = (178,1) . Please help (: import keras from keras.models import Sequential from keras.layers import Dense import numpy as np # number of wine classes classifications = 3 # load dataset dataset = np.loadtxt('wine.csv', delimiter=",") X = dataset[:,1:14] Y = dataset[:,0:1] # convert output values to one-hot Y = keras.utils.to

问题 I try to set up a simple text classification task with the SGDClassifier of scikit and try to get the top N predictions back including their probabilities. As sample training data I have the three classes apples lemons oranges with one document per class: in apples: 'apple and lemon' in lemons: 'lemon and orange' in oranges: 'orange and apple' I now want to predict the three test docs 'apple', 'lemon' and 'orange' and would like to get the Top-2-Predictions per document, including their