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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hinge loss vs logistic loss advantages and disadvantages/limitations

Hinge loss can be defined using $\text{max}(0, 1-y_i\mathbf{w}^T\mathbf{x}_i)$ and the log loss can be defined as $\text{log}(1 + \exp(-y_i\mathbf{w}^T\mathbf{x}_i))$ I have the following questions: ...
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16 views

maximum mis-classification loss of hinge loss

In the plots and in some lecture notes, I read that hinge loss is bounded between (0,2). But I can not understand that. By definition, hinge loss is (standard one) ...
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61 views

showing that $\bar{X}$ is inadmissible by comparing with $\max(\bar{X},2)$ under squared error loss function

suppose $X_1,X_2,\ldots,X_n$ be a random sample of $N(\theta,1), \theta>2$. how can I show $\bar{X}$ is inadmissible estimator Compared to $\max(\bar{X},2)$ under Squared error loss function
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26 views

Does maximum likelihood minimize a kind of generalized “0-1 loss”?

A very good point was raised here about how the optimal betting strategy under 0-1 loss was to bet on the mode, while under MSE loss the optimal strategy was to bet on the mean. Maximum likelihood ...
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3answers
46 views

Equating the two equations of Ridge Regression

I'm studying Ridge Regression now and I'm having a bit of trouble understanding how to relate the two equations that pop up when I read about it. There is the coefficient estimate: $$\hat{\beta} = ...
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94 views

What would be an example of when L2 is a good loss function for computing a posterior loss?

L2 loss, together with L0 and L1 loss, are three a very common "default" loss functions used when summarising a posterior by the minimum posterior expected loss. One reason for this is perhaps that ...
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31 views

Implementing WARP Loss (Gradient Computation)

I am trying to implement the WARP Loss in Torch, as defined in the WSABIE paper: http://www.thespermwhale.com/jaseweston/papers/wsabie-ijcai.pdf The Algorithm is as follows: The Algorithm ...
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27 views

Value of the loss function and Classification Errors in gbm package (R)

I have a simple problem of classification (0s and 1s) using adaboost loss function. When I check the components of a boosted model using the gbm package I see: ...
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26 views

Loss function from Posterior log likelihood

I am trying to implement Bayesian Logistic Regression and was looking at this paper http://www.stat.columbia.edu/~madigan/PAPERS/techno.pdf. The authors in this paper had assumed the $\beta$s to have ...
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18 views

Loss function for rank deficient covariance matrices?

I'm trying to compare the efficiency of different estimators of the covariance matrix of a particular type of multivariate normally distributed data. This comparison, as well as the estimation process ...
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1answer
57 views

Get distribution for aggregate loss using Monte Carlo

I am given two data sets containing dates and losses (in some currency). Given a distribution for the amount of losses and an (a,b,0) distribution for frequency of losses, how can I use Monte Carlo ...
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1answer
69 views

Designing Asymmetric regression (assymettric loss for regression)

I have a hybrid classification/regression problem.The predicted value can be assumed to be centred around 0. I want to penalize the predictor more, if the predicted value and actual value have ...
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1answer
106 views

minimax property of sample mean

Suppose $X_1,X_2,\ldots,X_n$ are iid $\mathcal{N}(\mu,\sigma^2)$, where $\sigma$ is known, but $\mu$ is not. We wish to construct a confidence interval of length $L$ (given) for $\mu$. Is it true that ...
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1answer
28 views

Estimating conditional variance y|x

I am building a predictor for $y = f(x)$ using training samples ${(x_i, y_i)}$ (assume) drawn i.i.d from some distribution $p(x,y)$, by optimising the empirical L2-loss: $f(x) = argmin_f \; \sum_i ...
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24 views

Incorporating Risk Aversion in Bayesian Expected Loss functions

In Berger's Statistical Decision Theory and Bayesian Analysis, he presents the following expected loss function for decision theory: $\rho(\pi^*,a)=\int_\Theta L(\theta,a)d\pi^*(\theta)$ Where ...
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1answer
102 views

Bayesian Estimation: Bernoulli and Quadratic Loss Function

I am trying to understand a solution to this problem (I am a very beginner in Bayesian statistics) and I am terribly confused so I would appreciate it if someone could explain to me how exactly this ...
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31 views

R- Cost function in glm

I need to introduce a cost function in a logistic model (I'm using R). As I saw from this question, we can introduce costs in cv.glm. But I don't know how to introduce it in glm. My cost function ...
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39 views

Baseline for Precision-Related Metrics

When working with ROC-AUC as a metric for binary classification, one often considers a value of 0.5 as a baseline from a random classifier (i.e. a data-blind classifier that randomly classifies test ...
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49 views

cost matrix, unbalanced class, oversampling and threshold probability

Let's suppose I have a cost matrix with TP=+90 FP=-10 TN=0 and FN=-10, and that the class is unbalanced. I need to capture the costs in my decision. To do so, I always consider the probability ...
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89 views

Loss Functions and Evaluation Metrics

Do you have to evaluate with the same (or equivalent) loss function for model selection purpose? Say you have bunch of models to select. A loss function of one model in training stage is different ...
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49 views

How to do optimization using asymmetrical loss functions (LINEX)

Long time Lurker, first time asking. For a research paper, i'm required to optimize some parameters of a certain function using an asymmetrical loss function, specifically LINEX and compare it to the ...
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1answer
99 views

what does a negative logloss value indicate

I am using logloss python function provided here and I am getting results as -2.99 when I use a machine learning algorithm on my dataset. What does that mean? The algorithm's predictions are bad (or) ...
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17 views

Would knowing the underlying distribution for our data affect how boosting searches for its predictor or how it minimizes the exponential loss?

Assume that the goal of Machine learning is to find a function that is able to minimize the generalization/expected/true error (assuming that the underlying distribution is fixed but unknown): $$E(f) ...
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25 views

Why is it necessary to assume that examples and labels are drawn from a joint distribution in empirical loss minimization?

Multiple sources have indicated that when trying to minimize empirical loss, $1/N \sum_i L(f(x_i, w), y_i)$, where $L$ is some loss function, $y_i$ is the true label, and $f(x_i, w)$ is the predicted ...
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18 views

How to define my custom cost function to be used in (stochastic) gradient descent?

I have a text classification problem were the classes are 20 cities and the input is text Bag of word features. I am using Logistic Regression and my cost function is negative log likelihood: ...
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21 views

Is there a version of Buhlmann-Straub credibility that uses an non-fixed $\theta_i$?

Everything I've read about Buhlmann-Straub credibility assumes a fixed $\theta_i$ (the unknown parameter that $X_{ij}$, the variable of interest, depends on). Does anyone know of a version where theta ...
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531 views

Multi-class logarithmic loss function per class

In a multi-classification problem, we define the logarithmic loss function $F$ in terms of the logarithmic loss function per label $F_i$ as: $$ F = -\frac{1}{N}\sum_{i}^{N}\sum_{j}^{M}y_{ij} \cdot ...
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15 views

In-sample likelihood ratio test for losses

Probably a very simple question for practitioners but I am doing this the very first time: I have written a programm for the estimation of a conditional variance model (HEAVY) of return data and an ...
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1answer
114 views

Comparing residuals between OLS and non-OLS regressions

Suppose you want to estimate a linear model: ($n$ observations of the response, and $p+1$ predictors) $$\mathbb{E}(y_i) = \beta_0 + \sum_{j=1}^p \beta_j x_{ij}$$ One way to do this is through the OLS ...
6
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149 views

Choosing between loss functions for binary classification

I work in a problem domain where people often report ROC-AUC or AveP (average precision). However, I recently found papers that optimize Log Loss instead, while yet others report Hinge Loss. While I ...
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79 views

MHT: Pre-Selecting statistical tests without Bias

Summary: The last formula boxed in red (which is a modified log likelihood from logistic regression) is a special non-differentiable loss function that is adapted to contain a Bonferroni correction in ...
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41 views

Squared Error vs Absolute Error loss functions [duplicate]

The two most popular types of loss functions are 1) squared error: $L(y,f(x))=(y-f(x))^2$ --> best estimate is the $E(Y|x) $ 2) absolute error: $L(y,f(x))=|y-f(x)|$ --> best estimate is the ...
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2answers
520 views

Cost function of neural network is non-convex?

The cost function of neural network is $J(W,b)$, and it is claimed to be non-convex. I don't quite understand why it's that way, since as I see that it's quite similar to the cost function of logistic ...
2
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1answer
49 views

regularized loss function

I'm fitting a classifier with cross-entropy loss (i.e. Bernoulli likelihood). Some examples are very clearly associated with one class or the other, and despite some attempts at regularization, the ...
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1answer
69 views

Loss function that relates ROPE with HDI?

In Doing Bayesian Data Analysis (link to the book) and Bayesian Estimation Supersedes the t-Test, J. Kruschke proposes using the following criterion to reject or accept the null hypothesis in a ...
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259 views

Percentile Loss Functions

The solution to the problem: $$ \min_{m} \; E[|m-X|] $$ is well known to be the median of $X$, but what does the loss function look like for other percentiles? Ex: the 25th percentile of X is the ...
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20 views

How to predict extremes of a continuous response variable?

Suppose I have a response $Y_t \sim N(0,\sigma^2_Y)$ and features $X_{i,t} \sim N(0,\sigma^2_{X_i})$ for $i \in \{1,...,100\}$. $Y_t$ conforms to a linear model $Y_t = a + \sum_{i=1}^{100} b_i ...
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49 views

Wilcoxon-Mann-Whitney as a loss function

I'm reading a paper where the authors are using Wilcoxon-Mann-Whitney loss function while minimizing an objective function. As the authors say in the paper, the role of the loss function is to give a ...
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152 views

Liblinear types of solver

There is many variants of type of solver in liblinear but I don't understand their differences.Which one I must choose? Also why data must be scaled? duo to some numerical issues? ...
2
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1answer
165 views

Interpretation of logloss value

Does anyone have a interpretation of a logloss value? Am I correct to assume that values closer to 0 and 1 are more likely to be an indication that the predicted value is incorrect? Thanks.
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1answer
53 views

Forcing a particular false positives rate in a learning algorithm

I have a learning algorithm that classifies points as 0 or 1 (haven't settled on which one to implement yet). Of the points I classify as 1, I want to ensure that the number of points correctly ...
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254 views

Comparable traing and test cross-entropies result in very different accuracies

Premises I'm training a convolutional neural network (ConvNet) on 51 subclasses in the ImageNet dataset. In order to keep an eye on overfitting, I have been suggested to plot training and testing ...
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41 views

What is a robust way to find the max of $n$ independent, non-identical random variates?

Suppose I observe $n$ random variates along with their variance (but not mean) and I'd like to select the one with the largest mean as frequently as possible. The procedure must be memoryless--you ...
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1answer
65 views

Relation between Scoring rule and Loss function in Parameter estimation and model selection?

Initially, I had only heard of MLE and use it for almost everything, e.g. point estimate and model selection (with some penalty). Then, MSE appeared, which seems to play the same role as MLE does. I ...
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82 views

Question about computing Bayes Error - with or without loss function?

I am new to Bayesian Decision Theory and don't understand the following concept: So from what I understood, the Bayes error is used to report the performance of a Bayes classifier in terms of the ...
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88 views

How to design a cost function that has different weights for different types of classification errors?

I'm trying to design a continuous loss function for a logistic classifier. Suppose I have the following confusion matrix: [tn fp fn tp] I want the loss ...
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3answers
300 views

Importance of optimizing the correct loss function

I want to understand the importance of optimizing the correct loss function. Say that I am building a linear regression model $p$ for predicting some values $y_1,\ldots,y_n$. I choose to fit my ...
2
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1answer
369 views

Minimalizing absolute cost function error

I got two questions. 1. I know that in predictive analytics contests, when faced to yes/no problems, with the absolute cost function $f(x) = \frac{1}{n}\sum_{i=1}^n\lvert x_i-\hat x_i\rvert$ the ...
3
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1answer
368 views

LASSO with L1 loss function

I've been trying to figure out a way to perform LASSO with L1 loss function (instead of the L2 loss) but have been completely dumfounded as to how. I've attempted to use the flare package's ...
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1answer
63 views

Is the F-1 score symmetric?

Below is the report of my out-of-bag precision, recall and f-1score when using ...