classification

问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 7 years ago . Do you know of any good c++ svm libraries out there I tried libsvm http://www.csie.ntu.edu.tw/~cjlin/libsvm/ but so far I'm not

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 am training ML logistic classifier to classify two classes using python scikit-learn. They are in an extremely imbalanced data (about 14300:1). I'm getting almost 100% accuracy and ROC-AUC, but 0% in precision, recall, and f1 score. I understand that accuracy is usually not useful in very imbalanced data, but why is the ROC-AUC measure is close to perfect as well? from sklearn.metrics import roc_curve, auc # Get ROC y_score = classifierUsed2.decision_function(X_test) false_positive_rate,

问题 Having real trouble figuring out where i'm going wrong on this one. Building a system using WEKA in java to study associations and am trying to implement the Apriori algorithm. Currently this is the code: package model; import weka.associations.*; import weka.core.Instances; import weka.core.converters.ConverterUtils.DataSource; public class Apriori { public static void main(String args[]) throws Exception { String dataset = "/Users/andrew/workspace/Movies/src/data/tagsfinal.arff"; DataSource

问题 I trained my CNN classifier (using tensorflow) with 3 data categories (ID card, passport, bills). When I test it with images that belong to one of the 3 categories, it gives the right prediction. However, when I test it with a wrong image (a car image for example) it keeps giving me prediction (i.e. it predicts that the car belongs the ID card category). Is there a way to make it display an error message instead of giving a wrong prediction? 回答1: This should be tackled differently. This is

问题 I am working on an NLP classification project using Naive Bayes classifier in Weka. I intend to use semi-supervised machine learning, hence working with unlabeled data. When I test the model obtained from my labeled training data on an independent set of unlabeled test data, Weka ignores all the unlabeled instances. Can anybody please guide me how to solve this? Someone has already asked this question here before but there wasn't any appropriate solution provided. Here is a sample test file:

问题 I have a image that includes object and some unwanted region (small dots). I want to remove it. Hence, I use some morphological operator example 'close' to remove. But it is not perfect. Do you have other way to remove more clear? You can download example image at raw image This is my code load Image.mat %load Img value Img= bwmorph(Img,'close'); imshow(Img); 回答1: You might prefer a faster and vectorized approach using bsxfun along with the information obtained from bwlabel itself. Note:

问题 I've trained a classifier in Weka, and I'm able to use it on test data. Additionally, I can opt to display the classifier's predictions in the log window for this test data. However, for my current project, it would be convenient for me to be able to get this data in CSV format. Is this possible in Weka ? Is it only possible when using the command line (something I'll eventually move towards) ? I could always save the entire buffer result to a text file, but in that case, I would have to

问题 I am using the LogisticRegression() method in scikit-learn on a highly unbalanced data set. I have even turned the class_weight feature to auto . I know that in Logistic Regression it should be possible to know what is the threshold value for a particular pair of classes. Is it possible to know what the threshold value is in each of the One-vs-All classes the LogisticRegression() method designs? I did not find anything in the documentation page. Does it by default apply the 0.5 value as

问题 There are two classes, let's call them X and O. A number of elements belonging to these classes are spread out in the xy-plane. Here is an example where the two classes are not linearly separable. It is not possible to draw a straight line that perfectly divides the Xs and the Os on each side of the line. How to determine, in general, whether the two classes are linearly separable? . I am interested in an algorithm where no assumptions are made regarding the number of elements or their