Questions tagged [metric]

A metric is a function that outputs a distance between 2 elements of a set & meets certain strict criteria (some 'distance' functions are not metrics).

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Judging a model through the TP, TN, FP, and FN values

I am evaluating a model that predicts the existence or not existence of a "characteristic" (for example, "there is a dog in this image") using several datasets. The system outputs ...
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1 vote
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Why RandomForestRegressor.score() return a coefficient of determination? [duplicate]

In ScikitLearn's method RandomForestRegressor.score(X, y), the coefficient of determination R_2 is returned as a metric of the ...
20 views

Kfold cross val in Regression model

How to use K-fold CV to evaluate my regression model performance to calculate the R2, MAE and MSE in the train set to make the model more robust? This code below refers to the tuned model and I'm ...
11 views

"ROC AUC reflects the likelihood that a random positive instance will be located to the right of a random negative instance". How come? [duplicate]

According to this webpage, ROC AUC reflects the likelihood that a random positive (red) instance will be located to the right of a random negative (gray) instance. Would you please explain this ...
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1 vote
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Why do we use both Kullback–Leibler divergence vs FID as metric for evaluating distribution distance?

Kullback–Leibler divergence is used in VAE objective function, while FID measures the quality of images of generative models. But they both measure distribution distance. Why do we have them as ...
27 views

Survival analysis: Use of "legible" metrics like RMSE

I apply a Cox proportional hazards model to some machine failure data and I want to know, "how good" my model is. Metrics like RMSE or MAE are said to be not suitable for this kind of model, ...
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Potential evaluation based on the coherence of predicted value with actual data

I have the following data over time: that means data collected for a single variable like CPU usage in lowest, highest, and average mode over time every 5 mins (data granularity = 5mins) like the ...
• 441
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On using the loss as a metric?

The context is model evaluation in supervised learning. I am coming from a numerical optimisation background. For me it is quite natural to use the loss of the model (what we optimise during training) ...
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15 views

Metric for Bayesian ground truth recovery

I am developing a new Bayesian model and want to compare it to already existing Bayesian models with the same hyperparameters using a simulation study. I generated 50 datasets and fit 4 different ...
1 vote
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What metric should I use for a Regression model with a gamma distributed target?

Background I'm building a regression model on insurance data to predict the losses associated with a policy. I'm running an Optuna optimisation function to help me with this, but I'm struggling with ...
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From pointwise convergence to uniform: metrics

Let $\mu_\theta$ be the limit of an empirical measure $\mu_{n, \theta}$. $\theta \in \Theta$ and $\Theta$ is a compact set. Morever, the maps $\theta \rightarrow \mu_{n, \theta}$ and \$\theta \...
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