# Questions tagged [precision]

Precision is about variability while accuracy (in contrast to precision) is about bias. This tag pertains to measurement or estimation; use [precision-recall] when talking about classifiers.

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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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### Precision calculation for Test data

I have a trained multiclassification (4 different labels) ML model for which I calculated Accuracy and Precision using Confusion Matrix . Now for the developed model, I give some test data without ...
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### What is the proper formatting of very low p values? [closed]

Generally, I believe in giving exact p values rather than something like " p < 0.05". But, sometimes, the p value is extremely low, on the order, say, of $10^{-10}$ or even lower. Under ...
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### Understanding order of operations in formula

Can someone help me understand the order of operations for this formula? Lets say: y_estimated = 10, 14, 11 #three different estimates that will be subbed into the formula y_true= 12 R=3 I think it ...
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1 vote
963 views

### Does it matter if real data will be imbalanced, if the ML model was trained on a balanced dataset?

I have trained a machine learning model (supervised, classification, LinearSVC) on a balanced dataset, which produces relatively good results on the test data. I am happy with the numbers, but not ...
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### Can I conclude that the classifier is always good when Precision-Recall Curve above the baseline?

I used logistic regression for highly imbalaned data (1=0.6% , 0=99.4%) Since PR curves are sensitive to imbalance, so i used it, but I don't know how to interpret graph appropriately. This is PR-...
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We have 2 Classification models (Random forest on balanced Data Set), the first one classify a Bank's client as a Churner (closing his account) or an active client, the second one classify a Bank's ...
266 views

### High precision and low recall but with a balanced dataset

When i evaluated my model (CatBoost classifier), I noticed that my model has high precision and low recall (Recall: 0.59, Precision: 0.89) but the classes are perfectly balanced (1: 45.5, 2: 54.5) and ...
31 views

### Determining the precision of a range

Supposing that I have 2 measured values which cover a range Y and Z and the actual value "X" is somewhere in the middle. How can I calculate the precision of the range? I expected to ...
1 vote
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### Sensibly dealing with the precision available in likelihood functions

I am running some simulations involving Bayesian updating of prior odds given a succession of measurements and corresponding likelihood functions. Inevitably, repeated multiplication of the prior by ...
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### classification ML model: probability of positive label knowing the model score

Question at the intersection of ML and statistics. I built a binary classification ML model, that for each input observation x outputs the probability p(x) in (0,1) that x belongs to the positive ...
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1 vote
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### Are evaluation metrics computed on training dataset?

Based on my own studies and questions on this site, my understanding is that evaluation metrics (accuracy, precision, recall) are only calculated on the test dataset. The training dataset is used to ...
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### How can precision be less than one in Leave-One-Subject-Out binary classification if each subject contains only one class

Say I'm trying to classify a medical condition. Theres only two classes: Sick and Healthy. I build a model and I can't split the data because I don't want data from the same patient being in training ...
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### Meta-analysis on percentage of total calorie intake

We are looking at a number of studies which have profiled nutrient information which contributes to total daily calorie intake. For example, fibre might make up 5% of daily total calorie intake. My ...
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### Is bias and variance equivalent to accuracy vs precision?

I looked at some graphs of high bias and high variance, and I don't really understand how the bias vs variance tradeoff is any different from accuracy vs precision?
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### Inaccurate parameter estimates for state-space models?

I'm modeling time series using dynamic linear/state-space models, and was surprised by how inaccurate the estimates of model parameters can be, even for fairly long series. In particular, I'm ...
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### What is the average precision in the case of no positives for a given category in the context of object detection

In attempting to calculate the average precision of an object detection model, I am wondering about an edge case. Suppose at evaluation time that for a given category, that no detections of that ...
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### How many significant figures should I report for a regression equation?

I have a regression equation that I am trying to include as part of a study predicting a variable of interest using a linear proxy using a simple ordinary least squares function. I did the regression ...
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### Precision-recall curve for two class problem

I'm wondering how to compute the precision-recall curve for the following problem: Let's say the true values are [1 0 1 1 1] Predicted values are [1 1 0 1 1] We have two values only that were not ...
905 views

### Why adjusting for randomization stratification factors in the model improves the precision of estimators for treatment effect?

I assume you will already get balanced treatment assignment within the randomization strata. Why would we still gain improvement in precision by adjusting for these factors in the model? Does not ...
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### Precision-Recall Curve Intuition for Multi-Class Classification

I am running a CNN image multi-class classification model with Keras/Tensorflow and have established about a 90% overall accuracy with my best model trial. I have 10 unique classes I am trying to ...
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1 vote
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### Question about OLS and BLUE in the presence of hetereoscadasticity and robust standard errors

My understanding that if errors are non-spherical, OLS is no longer the minimum variance linear unbiased estimator (assume the error terms are fully independent of all covariaties- so unbiasedness ...
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