# Questions tagged [log-loss]

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### Understanding cross entropy loss

The formula for cross entropy loss is this: $$-\sum_iy_i \ln\left(\hat{y}_i\right).$$ My question is, what is the minimum and maximum value for cross entropy loss, given that there is a negative sign ...
36 views

### Classification Models giving probabilities at extreme end only

I am building a binary classification model with proportion of 1 is at only 3% and total 70000 data points.I have 5 variables out of which 3 are coming out to be important. I have built model using ...
7 views

### Output value of a gradient boosting decision tree node that has just a single example in it

The general gradient boosting algorithm for tree-based classifiers is as follows: Input: training set $\{(x_{i},y_{i})\}_{i=1}^{n},}\{(x_{i},y_{i})\}_{i=1}^{n},$a ...
46 views

### Logarithmic loss vs Brier score vs AUC score

I have a dataset with two classes of elements. I also have two methods which assign (complementary) probabilities to each element in the dataset of belonging to either class. Given that I work with ...
37 views

### Negative Log-Likelihood Loss with Gibbs distribution for beta approaching infinity

This might be more of a math question, but since it concerns ML, I'll ask it here. In "A tutorial on energy-based learning" (LeCun et al., 2006), on page 15, section 2.2.4 about the Negative Log-...
32 views

### Use of sigmoidal function in output layer to predict continuous values and correct use of the loss function

Lets supose that a neural network is utilized to map a set of training data to a continuous interval between 0 and 1 utilizing a sigmoidal function on its output layer. Is it correct to optimize the ...
24 views

### binary cross entropy vs multi cross entropy

i am new to neural networks I know that multi class entropy is same as binary class entropy when the categories are only (0,1), but can some one explain it mathematically with an example that ...
19 views

### Gaussian MLP output maximum Likelihood optimization

I have problems traing networks using a Gaussian MLP as output. The output is split into on fully-connected layer that estimates expectation and another that estimates log(variance). See Auto encoding ...
25 views

### Loss computation for Gaussian MLP output layer of Variational Auto Encoder

I use a Variational Auto Encoder with Gaussian MLP as output according to Auto encoding variational Bayes appendix C I wanted to use the bound: I calculate the reconstruction loss by first ...
31 views

### How to compare log-loss across similar classification models with different baseline probabilities?

Suppose I have two datasets, A and B, which share a feature vector $X$ but have different units of analysis (e.g. people from two different countries). I have trained classifiers with the same model ...
183 views

### Why Log loss, AUC and precision & recall change differently when class imbalance problem is tackled?

I have a dataset and I'm working on a binary classification task with it. It has a class imbalance problem where False class versus ...
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### Can boosting be defined through a modification of weights in a binary classification problem?

Let's assume that we have a binary classification problem (our labels are only 0 and 1). We try to find a model that generates probabilities to observer 1. We measure quality of the model by log loss: ...
32 views

### How to interpret plot that compares log loss, hinge loss, and squared error loss

In books and articles that compare different loss functions, authors very often make the following plot. The following comes from Bishop's PRML book, with the caption Plot of the ‘hinge’ error ...
275 views

### How does Cross-Entropy (log loss) work with backpropagation?

I am having some trouble understanding how Cross Entropy would work with backpropagation. For backpropagation we exploit the chain rule to find the partial derivative of the Error function in terms of ...
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### Gradient boosting for binary outcome - terminal nodes estimate (using R gbm)

I have been searching for the answer for the below query quite a long time and found a few answers (see: interpretation of gbm single tree prediction in pretty.gbm.tree or R Package GBM - Bernoulli ...
265 views

### Interpreting Inf (Infinity) as answer to logloss function?

so I am using this logloss function logLoss = function(pred, actual){ -1*mean(log(pred[model.matrix(~ actual + 0) - pred > 0])) } sometimes it is correctly ...
10k views

### logloss vs gini/auc

I've trained two models (binary classifiers using h2o AutoML) and I want to select one to use. I have the following results: ...
8k views

### Log Loss function in scikit-learn returns different values

I have been trying to wrap my head around the log loss function for model evaluation. I understand how the value is calculated after doing the math by hand. In the python module ...
450 views

### Smoothing/shrinking the predicted probability of a classifier to reduce live logloss

Let us assume we work on a 2 -class classification problem. In my setting the sample is balanced. To be precise it is a financial markets setting where up and down have approximately 50:50 chance. The ...
873 views

### Probability Calibration messes Reliability

I have about 1000 samples with 20 features and I'm using Random Forest to predict a binary class. I'm trying to apply the probability calibration process as described on scikit using ...
346 views

### Sudden increase in log-loss on training set with H2O GBM

I am performing a hyperparameter grid search for a GBM classifier in H2O, running version 3.10.4.8 (on top of python 3.5.3). This is a multiclass problem (~40 classes). As a first test, I tried a ...
2k views

### Performance Metrics for Imbalanced Classification

I'm trying to fit multiple Stochastic Gradient Descent models to a dataset where the target (binary target, 0 or 1) is very imbalanced, i.e the success rate is about 0.0001. Out of all the models I'...
888 views

### logloss equivalent for poisson regression

I have a Poisson regression model, and I would like to measure the discrepancy between actual counts and predicted counts. For binary classification model, the log-loss metrics fits for this purpose. ...
23k views

### What's considered a good log loss?

I'm trying to better understand log loss and how it works but one thing I can't seem to find is putting the log loss number into some sort of context. If my model has a log loss of 0.5, is that good? ...
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### How do I choose the right loss function , logarithmic loss for 0/1 or exponential loss for -1/1? [duplicate]

As we know , we have two kinds of presentation in binary classification , one is 0/1 and the other is -1/1 . For 0/1 case , we often use "negative logarithmic likelihood" loss function for it , also ...
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### Am I correct to get negative cross_val_score when I am using neg_log_loss in scikit-learn 0.18?

I am working on a data set to predict if someone is going to donate blood from UCI Data repository The criteria of judging the solution is log loss So I implement the cross_val_score function from ...
5k views

### How does the L2 regularization penalize the high-value weights

I am reading about regularization in machine learning model. I want to understand how mathematically the L2 term penalizes the high-value weights to avoid overfitting? Any explanation?
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### Log loss: model vs benchmark. Minimum/optimal value

So, I've built a multinomial logit model (with 4 classes) that has log loss equal 0.945. My benchmark model (probabilities equal classes distribution in train sample) gives log loss 1.131. How can I ...
8k views

### optimizing auc vs logloss in binary classification problems

I am performing a binary classification task where the outcome probability is fair low (aroung 3%). I am trying to decide whether to optimize by AUC or log-loss. As much as I have understood, AUC ...