precision-recall

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? 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 manner. For each predicted label its only its score is computed, and then these scores are aggregated over

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, true_positive_rate, thresholds = roc_curve(y_test, y_score) roc_auc = auc(false_positive_rate, true

I am building a multi-class classifier with Keras 2.02 (with Tensorflow backend)，and I do not know how to calculate precision and recall in Keras. Please help me. Yasha Bubnov Python package keras-metrics could be useful for this (I'm the package's author). import keras import keras_metrics model = models.Sequential() model.add(keras.layers.Dense(1, activation="sigmoid", input_dim=2)) model.add(keras.layers.Dense(1, activation="softmax")) model.compile(optimizer="sgd", loss="binary_crossentropy", metrics=[keras_metrics.precision(), keras_metrics.recall()]) As of Keras 2.0, precision and recall

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? 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 array): array([[ 963, 0, 0, 1, 0, 2, 11, 1, 2, 0], [ 0, 1119, 3, 2, 1, 0, 4, 1, 4, 1], [ 12, 3, 972, 9, 6, 0,