precision-recall

问题 I am currently learning Information retrieval and i am rather stuck with an example of recall and precision A searcher uses a search engine to look for information. There are 10 documents on the first screen of results and 10 on the second. Assuming there is known to be 10 relevant documents in the search engines index. Soo... there is 20 searches all together of which 10 are relevant. Can anyone help me make sense of this? Thanks 回答1: Recall and precision measure the quality of your result.

问题 Background I have a multi-label classification problem with 5 labels (e.g. [1 0 1 1 0] ). Therefore, I want my model to improve at metrics such as fixed recall, precision-recall AUC or ROC AUC. It doesn't make sense to use a loss function (e.g. binary_crossentropy ) that is not directly related to the performance measurement I want to optimize. Therefore, I want to use TensorFlow's global_objectives.recall_at_precision_loss() or similar as loss function. Relevant GitHub: https://github.com

问题 I have performed GaussianNB classification using sklearn. I tried to calculate the metrics using the following code: print accuracy_score(y_test, y_pred) print precision_score(y_test, y_pred) Accuracy score is working correctly but precision score calculation is showing error as: ValueError: Target is multiclass but average='binary'. Please choose another average setting. As target is multiclass, can i have the metric scores of precision, recall etc.? 回答1: The function call precision_score(y

问题 I trained a basic FFNN on a example breast cancer dataset. For the results the precision_recall_curve function gives datapoints for 416 different thresholds. My Data contains 569 unique prediction values, as far as I understand the Precision Recall Curve I could apply 568 different threshold values and check the resulting Precision and Recall. But how do I do so? is there a way to set the number of thresholds to test with sklearn ? Or at least an explanation of how sklearn selects those

问题 I am really confused about how to calculate Precision and Recall in Supervised machine learning algorithm using NB classifier Say for example 1) I have two classes A,B 2) I have 10000 Documents out of which 2000 goes to training Sample set (class A=1000,class B=1000) 3) Now on basis of above training sample set classify rest 8000 documents using NB classifier 4) Now after classifying 5000 documents goes to class A and 3000 documents goes to class B 5) Now how to calculate Precision and Recall

问题 I'm using Python and have some confusion matrixes. I'd like to calculate precisions and recalls and f-measure by confusion matrixes in multiclass classification. My result logs don't contain y_true and y_pred , just contain confusion matrix. Could you tell me how to get these scores from confusion matrix in multiclass classification? 回答1: Let's consider the case of MNIST data classification (10 classes), where for a test set of 10,000 samples we get the following confusion matrix cm (Numpy

问题 I have 10+ features and a dozen thousand of cases to train a logistic regression for classifying people's race. First example is French vs non-French, and second example is English vs non-English. The results are as follows: ////////////////////////////////////////////////////// 1= fr 0= non-fr Class count: 0 69109 1 30891 dtype: int64 Accuracy: 0.95126 Classification report: precision recall f1-score support 0 0.97 0.96 0.96 34547 1 0.92 0.93 0.92 15453 avg / total 0.95 0.95 0.95 50000

问题 I am aware of the concept of Precision as well as the concept of Recall. But I am finding it very hard to understand the idea of a 'threshold' which makes any P-R curve possible. Imagine I have a model to build that predicts the re-occurrence (yes or no) of cancer in patients using some decent classification algorithm on relevant features. I split my data for training and testing. Lets say I trained the model using the train data and got my Precision and Recall metrics using the test data.

问题 I am aware of the concept of Precision as well as the concept of Recall. But I am finding it very hard to understand the idea of a 'threshold' which makes any P-R curve possible. Imagine I have a model to build that predicts the re-occurrence (yes or no) of cancer in patients using some decent classification algorithm on relevant features. I split my data for training and testing. Lets say I trained the model using the train data and got my Precision and Recall metrics using the test data.

问题 My question is relevant to this question. I am interested in computing Precision-Recall Curve (PRC) and area under PRC. I found a nice R package PRROC to do both tasks. According to package description (page 5) for function pr.curve , you have to give 2 parameters. 1) the classification scores of datapoints belonging to positive class only 2) the classification scores of datapoints belonging to negative class only (See manual page 7). The example they provide is: # create artificial scores as