precision-recall

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 Confusion matrix: [[33229 1318] [ 1119 14334]] AUC= 0.944717975754 /////////////////////////////////////////

I'm trying to compute the Average Precision (and Mean Average Precision ) on the Oxford Building image dataset . Below there is the code that they provide for computing Average Precision. Notice that pos_set is the union of the "optimal" and "good" images from the ground trouth set, while junk_set is a set of not-relevant images. void OxfordTest::computeAp(std::vector<std::string> &ranked_list){ float old_recall = 0.0; float old_precision = 1.0; float ap = 0.0; size_t intersect_size = 0; size_t i = 0; size_t j = 0; for ( ; i<ranked_list.size(); ++i) { if(!pos_set.count(ranked_list[i])) std:

I would like to implement a custom metric in keras that calculates the recall assuming that the top k% most probable y_pred_probs 's are true. In numpy I would do it as follows. Sort the y_preds_probs. Then take the value at the k th index. Note k=0.5 would give the median value. kth_pos = int(k * len(y_pred_probs)) threshold = np.sort(y_pred_probs)[::-1][kth_pos] y_pred = np.asarray([1 if i >= threshold else 0 for i in y_pred_probs]) The answer from: Keras custom decision threshold for precision and recall is quite close but assumes that the threshold for deciding which y_pred 's are assumed

I want to calculate the precision , recall and f-score using libsvm in Python but I do not know how. I have found this site but I have not understand how to call the function, if you can help me through example. You can take advantage of scikit-learn , which is one of the best packages for machine learning in Python. Its SVM implementation uses libsvm and you can work out precision, recall and f-score as shown in the following snippet: from sklearn import svm from sklearn import metrics from sklearn.cross_validation import train_test_split from sklearn.datasets import load_iris # prepare

Can anyone help me to calculate F-measure collectively ? I know how to calculate recall and precision, but don't know for a given algorithm how to calculate one F-measure value. As an exemple, suppose my algorithm creates m clusters, but I know there are n clusters for the same data (as created by another benchmark algorithm). I found one pdf but it is not useful since the collective value I got is greater than 1. Reference of pdf is F Measure explained . Specifically I have read some research paper, in which the author compares two algorithms on the basis of F-measure, they got collectively

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. But HOW can I draw a P-R curve now? On what basis? I just have two values, one precision and one recall.

Accuracy, precision, recall and f-score are measures of a system quality in machine-learning systems. It depends on a confusion matrix of True/False Positives/Negatives. Given a binary classification task, I have tried the following to get a function that returns accuracy, precision, recall and f-score: gold = [1] + [0] * 9 predicted = [1] * 10 def evaluation(gold, predicted): true_pos = sum(1 for p,g in zip(predicted, gold) if p==1 and g==1) true_neg = sum(1 for p,g in zip(predicted, gold) if p==0 and g==0) false_pos = sum(1 for p,g in zip(predicted, gold) if p==1 and g==0) false_neg = sum(1