A function used to quantify the difference between observed data and predicted values according to a model. Minimization of loss functions is a way to estimate the parameters of the model.

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23 views

Is there an optimal loss function for dealing with imbalanced classes?

I'm aware that there are many ways of dealing with datasets where there is a strong class imbalance in the target variable: downsampling the more prevalent and less important class, over-weighting the ...
2
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1answer
37 views

Different definitions of the cross entropy loss function

I started off learning about neural networks with the neuralnetworksanddeeplearning dot com tutorial. In particular in the 3rd chapter there is a section about the cross entropy function, and defines ...
3
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1answer
39 views

Does “Log loss” refer to Logarithmic loss or Logistic loss?

I know I've seen it both ways, so is there a difference between the two, and which one is more commonly referred to?
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10 views

Per-instance cost-aware learning?

I have a situation where the misclassification cost depends on the instance, i.e. on the independent variables. In my training set I have for each instance the independent variables plus a vector of ...
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0answers
54 views
+50

Can I hack weighted loss function by creating multiple copies of data

Suppose we want to build a binary classifier with weighted loss, i.e., it penalize different types of errors (false positive and false negative) differently. At the same time, the software we are ...
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2answers
38 views

Loss function for spam detection like problems

I am working on a deep learning problem where wrong classifications of fake events are not problematic, but where the opposite case is very problematic. I suppose this is similar to how spam detectors ...
4
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0answers
58 views

What are the impacts of choosing different loss functions in classification to approximate 0-1 loss

We know that some objective functions are easier to optimize and some are hard. And there are many loss functions that we want to use but hard to use, for example 0-1 loss. So we find some proxy loss ...
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1answer
73 views

why cost function needs to be smooth, how this helps in learning?

From what I understand a smooth function 1-degree is a function whose first derivative is continuous? How this helps in estimating the parameters? What if the function is not smooth?
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0answers
43 views

Error measure and learning process - Stuck!

Problem : You have N data points y1 <= ... <= YN and wish to estimate a ' representative' value. 1) If your algorithm is to find the hypothesis h that minimizes the in sample sum of squared ...
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24 views

Single pass object detection

Let we have a set of images $\{\mathcal I_i\}_{i=1}^n$ with labels $\{\mathcal B_i\}_{i=1}^n$, where each $\mathcal B_i$ is a set of regions. The problem is to find a function that given image $\...
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0answers
56 views

How does an estimator that minimizes a weighted sum of squared bias and variance fit into decision theory?

Okay--my original message failed to elicit a response; so, let me put the question a differently. I will start by explaining my understanding of estimation from a decision theoretic perspective. I ...
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0answers
27 views

Increasing the learning rate on loss function saturation

I'm currently reading about neural networks, specifically how loss functions saturation can cause problems. During my studies, I was curious if one could remedy the problem during training of neural ...
2
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0answers
63 views

custom loss for mean absolute percentage error (MAPE) in xgboost [closed]

For now I do some task about regression, and in this case I use MAPE metric. I really need to try to use xgbost with custom objective (because other custom in my case has bad scores) , but i don't ...
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1answer
155 views

Gradient for logistic loss function

I would ask a question related to this one. I found an example of writing custom loss function for xgboost here: ...
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39 views

Is it ok to use symmetric loss function when evaluation metric is asymmetric?

I completely understand that it's ok to use a loss function different from the evaluation metric. For example, accuracy isn't computationally feasible to optimize directly since it's not ...
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1answer
55 views

loss function for data with noisy labels?

I'm trying to train a neural network for classification, but the labels I have are rather noisy (around 30% of the labels are wrong). The cross-entropy loss indeed works, but I was wondering are ...
1
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1answer
36 views

How to calculate the information loss of PCA?

How would we calculate the information loss of reducing dimensions using PCA ? Would it be the amount of variance loss if we skip certain eigenvectors after the PCA ?
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0answers
24 views

Unequal misclassification costs for SVC?

I wonder if there is a way to specify custom cost function in sklearn/python? (atm I use sklearn SVC) My real problem has 7 different classes, but to make it more clear lets assume that I want to ...
0
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1answer
51 views

Cross entropy-equivalent loss suitable for real-valued labels

I am building a model whose outputs are between 0-1 and the goal is to minimize a cost function over the predicted values and labels. So far everything seems to be easy but my labels are real-valued ...
1
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1answer
26 views

How best to summarize a predictive discrete distribution in a single number?

I have generated a predictive distribution for a future discrete observable outcome, and would like to generate a single value $p$ which we would most likely encounter when we perform the experiment ...
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1answer
16 views

Loss function/error measurement for allocation problem

I'm working on a prediction rule for an allocation problem. So, it's data like this: ...
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1answer
20 views

Scaling up random guess benchmark of log loss

On Quora it's said that for a random guess log loss is equal to ln(0.5) = ~0.693 That indeed implies that with a log loss of 0.69, you aren't doing any better than a random guess. OK, great. ...
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1answer
21 views

Cost-sensitive SVM with sklearn

Is there a direct cost-sensitive implementation of the SVM classifiers (CS-SVM) within the sklearn module? There are several ad hoc methods for the cost-sensitive SVM on "the market", but I am ...
2
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1answer
36 views

SVM cost function: old and new definitions

I am trying to reconcile different definitions of the soft-margin SVM cost / loss function in primal form. There is a "max()" operator that I do not understand. I learned about SVM many years ago ...
0
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1answer
92 views

Loss functions that act on real-valued output vectors (and NOT just on 1-hot vectors)

I am trying to modify Andrej Karpathy's char-RNN code. As far as I understand, the loss function used in his code for a LSTM is the Softmax function function (in the file model/LSTM.lua ). I ...
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9 views

incorporating the concept of 'coverage' in cross entropy loss

by coverage, I mean that I want my binary classifier (neural network) to perform EXTREMELY well on a large portion of the data (e.g. 95%), even if this means that it performs extremely poorly on the ...
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1answer
92 views

Scoring quantile regressor

Let's suppose that there is a real random variable $Y$ that is generated by some random process that depends somehow on vector $\vec x.$ I've built a model that for given $\vec x$ predicts $\tau$-...
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19 views

Loss function for tags reconstruction

I am trying to build function which reconstructs recipe ingredients by subset of them and with my loss function it predict only most popular ones. May be someone can suggest better loss function. ...
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0answers
236 views

choosing a loss function for gbm

I am using gbm to predict an imbalanced binary outcome, with the intent of obtaining a ranking by class probability estimation that produces a strong class ...
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0answers
28 views

Interpretation of the consistency property of a loss function

I am looking for an interpretation of the consistency property of a loss function used for classification (e.g., the SVM's hinge loss: $V(t)=\max(0,1-t)$). I copy from Wikipedia: Furthermore, it ...
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0answers
21 views

Difference between minimize risk and minimize misclassification probability

I am currently reading the book "Neural Networks for Pattern recognition". In Chapter 1.10, it said So far we have based our classification decisions on the desire to minimize the probability of ...
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1answer
48 views

Selecting a loss-function for k-fold cross-validation over shrinkage parameter

I am doing a penalized regression with categorical (ordinal) outcomes. I would like to select the shrinkage parameter $\lambda$ on the basis of cross-validation (CV). In this case, I have 50k ...
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1answer
47 views

Intuitive explanation of logloss

In several kaggle competitions the scoring was based on "logloss". This relates to classification error. Here is a technical answer but I am looking for an intuitive answer. I really liked the ...
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0answers
308 views

What loss function for multi-class, multi-label classification tasks in neural networks?

I'm training a neural network to classify a set of objects into n-classes. Each object can belong to multiple classes at the same time (multi-class, multi-label). I read that for multi-class problems ...
2
votes
1answer
70 views

Probabilistic classification and loss functions

I am trying to compare several binary classifiers. These classifiers (Gaussian Processes in my case, but it shouldn't matter) give me probabilistic predictions. Let's introduce some notations: $$y_i \...
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1answer
125 views

Is it okay to use cross entropy loss function with soft labels?

I have a classification problem where pixels will be labeled with soft labels (which denote probabilities) rather than hard 0,1 labels. Earlier with hard 0,1 pixel labeling the cross entropy loss ...
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1answer
64 views

Relationship between 0-1 Loss and error Type I and II in Neyman Pearson

In the context of hypothesis test $$H_0:\theta\in \Theta_0$$ $$H_1:\theta\notin \Theta_0$$. Find the relationship between the 0-1 loss defined by $$L(\theta,\delta)= \begin{cases} 1-\delta & \...
11
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1answer
131 views

Can cross-validation be helpful if we are interested only in modeling, not in forecasting?

Can cross-validation be helpful if we are interested only in modeling (i.e. estimating parameters), not in forecasting? I see how cross-validation is extremely useful if your goal is to make good ...
6
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1answer
261 views

Loss function Approximation With Taylor Expansion

As an example, take the objective function of the XGBOOST model on the $t$'th iteration: $$\mathcal{L}^{(t)}=\sum_{i=1}^n\ell(y_i,\hat{y}_i^{(t-1)}+f_t(\mathbf{x}_i))+\Omega(f_t)$$ where $\ell$ is ...
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0answers
44 views

How to define loss function for an unbalanced dataset?

I use neural network to do classification. But instead of outputing one label, I want to ouput four independent labels such as [-1,1,1,-1]. Each of them is either 1 or -1, indicating a classification ...
8
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2answers
196 views

Comparison between Bayes estimators

Consider the quadratic loss $L(\theta,\delta)=(\theta-\delta)^2$, with prior given $\pi(\theta)$ where $\pi(\theta)\sim U(0,1/2)$. Let $f(x|\theta)=\theta x^{\theta-1}\mathbb{I}_{[0,1]}(x), \...
2
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1answer
192 views

Is it common practice to minimize the mean loss over the batches instead of the sum?

Tensorflow has an example tutorial about classifying CIFAR-10. On the tutorial the average cross entropy loss across the batch is minimized. ...
3
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1answer
171 views

Training loss goes down and up again. What is happening?

My training loss goes down and then up again. It is very weird. The cross-validation loss tracks the training loss. What is going on? I have two stacked LSTMS as follows (on Keras): ...
3
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1answer
44 views

Error distributions and loss functions

If I'm right, using the Gaussian distribution in the maximum likelihood estimate yields the mean squared loss. Are there similar relationships between other distributions and losses (say Bernoulli ...
2
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0answers
16 views

Interplay of Training Class Sizes, Class Weights, Loss function and Decision Threshold

I am facing a two-class classification problem where: There is way more training data in class 1 than in class 0. Classifying a class 0 event as class 1 has a higher loss than classifying a class 1 ...
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0answers
22 views

Loss function selection for weighting errors differently

I am building a regression model where I want to score/optimize/train 'over-predictions' to be twice costly as under predictions. I am attempting to do this in R and hopefully with caret package. ...
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0answers
52 views

Intuition for Normalized Squared Loss error function?

In terms of optimization squared loss is perhaps the most common error function used for regression. I've seen another function named "Normalized Squared Loss" mentioned, described as The ...
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1answer
43 views

Weights and hinge loss/linear SVM

Say I have a dataset with this distribution Class A: 10 Examples Class B: 100 Examples Class C: 1000 Examples Hence, i am trying to build a classifier using linear SVM. Baring all concerns about ...
0
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0answers
55 views

Loss function for multiple predictions without ground truth

I'm searching for a loss function to determine the difference between two predictions made by several multi-label classifiers. Consider the following: Prediction of classifier A is [0.2,0.5,0.1,0.8] ...
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0answers
65 views

Loss Function for Multinomial Logistic Regression - Cannot find its derivative

For Multinomial Logistic Regression we can define the Loss Function in the following way: $J(\theta)=\frac{-1}{m}\sum\limits_{i=1}^m\sum\limits_{j=1}^k 1(y^{(i)}=j)\log(\frac{\exp(\theta_j^{T}x^{(i)})...