query: Log loss when classes are -1 and +1
positive_passages.docid: 18347
positive_passages.text: Why there are two different logistic loss formulation / notations?
negative_passages.docid: 154210
negative_passages.text: Elegant high precision `log1p`?
negative_passages.docid: 1224
negative_passages.text: Is it ok to transform a logarithm variable to z score
negative_passages.docid: 151342
negative_passages.text: Is there an optimal loss function for dealing with imbalanced classes?
negative_passages.docid: 9710
negative_passages.text: Quantile regression: Loss function
negative_passages.docid: 122421
negative_passages.text: Negative logLikelihood Kalman filter
negative_passages.docid: 34036
negative_passages.text: Positive correlation and negative regressor coefficient sign
negative_passages.docid: 55629
negative_passages.text: How to interpret a negative linear regression coefficient for a logged outcome variable?
negative_passages.docid: 107366
negative_passages.text: How to set class weights for imbalanced classes in Keras?
negative_passages.docid: 36088
negative_passages.text: Class imbalance in Supervised Machine Learning
negative_passages.docid: 12090
negative_passages.text: On Negative AIC Values
negative_passages.docid: 42624
negative_passages.text: Class imbalance strategies
negative_passages.docid: 39733
negative_passages.text: Validation loss is not decreasing
negative_passages.docid: 8061
negative_passages.text: Why must the base of a logarithm be a positive real number not equal to 1?
negative_passages.docid: 108007
negative_passages.text: Can anybody help me with this logarithm problem?
negative_passages.docid: 125575
negative_passages.text: Dimensional regularization: removing more than just logarithmic divergencies?
negative_passages.docid: 45874
negative_passages.text: What do 1.#INF00, -1.#IND00 and -1.#IND mean?