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32

F1Score is a metric to evaluate predictors performance using the formula F1 = 2 * (precision * recall) / (precision + recall) where recall = TP/(TP+FN) and precision = TP/(TP+FP) and remember: When you have a multiclass setting, the average parameter in the f1_score function needs to be one of these: 'weighted' 'micro' 'macro' The first one, '...


27

There is no relationship between these two metrics. Loss can be seen as a distance between the true values of the problem and the values predicted by the model. Greater the loss is, more huge is the errors you made on the data. Accuracy can be seen as the number of error you made on the data. That means: a low accuracy and huge loss means you made huge ...


19

The classification report is about key metrics in a classification problem. You'll have precision, recall, f1-score and support for each class you're trying to find. The recall means "how many of this class you find over the whole number of element of this class" The precision will be "how many are correctly classified among that class" The f1-score is ...


10

Let's be precise. "Distance" has lots of meanings in data science, I think you're talking about Euclidean distance. The Gaussian kernel is a non-linear function of Euclidean distance. The kernel function decreases with distance and ranges between zero and one. In euclidean distance, the value increases with distance. Thus, the kernel function is a more ...


9

Actually, accuracy is a metric that can be applied to classification tasks only. It describes just what percentage of your test data are classified correctly. For example, you have binary classification cat or non-cats. If out of 100 test samples 95 is classified correctly (i.e. correctly determined if there's cat on the picture or not), then your accuracy ...


7

Well actually these can give you different insights into your models errors. If $y$ is your target, $p$ your prediction and $e = p - y$ the errors: Mean Error: $ME = mean(e)$ In (-∞,∞), the closer to 0 the better. Measures additive bias in the error. Unbiased estimates should have the same mean as your target thus ME should be close to 0, if it's ...


7

CRPS is in a sense just the mean square error (MSE) of your predicted cumulative density function (CDF) and the true CDF. The CRPS generalizes the MAE (Mean Absolute Error) to the case of probabilistic forecasts. The CPRS is one of the most widely used accuracy metrics where probabilistic forecasts are involved. The CRPS is frequently used in order to ...


7

F1 will never be zero, but very near to zero for a bad classifier. If TP or TN is zero then there isn't any need to check F1.


6

And one last thing, is my result on X_train indicative that my features are informative enough to learn the target? or is the R² train score somehow biased? High scoring fits on training data does not necessarily indicate that your features are informative enough to learn the target in a general fashion. Only your cross validation scores can do so. Note :...


6

One major difference is that the F1-score does not care at all about how many negative examples you classified or how many negative examples are in the dataset at all; instead, the balanced accuracy metric gives half its weight to how many positives you labeled correctly and how many negatives you labeled correctly. When working on problems with heavily ...


5

The other answers give good definitions of accuracy and loss. To answer your second question, consider this example: We have a problem of classifying images from a balanced dataset as containing either cats or dogs. Classifier 1 gives the right answer in 80/100 of cases, whereas classifier 2 gets it right in 95/100. Here, classifier 2 obviously has the ...


5

As mentioned in other answers, traditionally cosine is used to measure similarity between vectors whereas Levenshtein is used as a string similarity measure, i.e. measuring the distance between sequences of characters. Nevertheless they both can be used in non-traditional settings and are indeed comparable: the vectors compared with cosine can for instance ...


5

In classification tasks for which every test case is guaranteed to be assigned to exactly one class, micro-F is equivalent to accuracy. The above answer is from: https://stackoverflow.com/questions/37358496/is-f1-micro-the-same-as-accuracy More detailed explanation: https://simonhessner.de/why-are-precision-recall-and-f1-score-equal-when-using-micro-...


5

It's a mistake on Wikipedia. $F_{1}$ as the harmonic mean is defined only at positive real numbers. $PRE$ or $REC$ could be equal 0 in case $TP=0$. Which provides to undefined result $F_1=\frac{0}{0}$.


5

First of all, precision and recall are not specific to image classification; they are relevant wherever there are two distinct "positive" and "negative" classes (for example, when you test an e-mail for "spam/not-spam", or a blood sample for "has virus/does not have virus"). You can read more on this question on Cross ...


4

According to me, it is not correct to co-relate loss with accuracy. Loss is used to optimize the hypothesis such that we can get best weights whereas accuracy is used to identify how well model is doing in term of correctly predicting the values. Model internally takes the reference of predict_proba() and returns 1 if probability is > .5 otherwise 0. ...


4

Aditya already mentioned rightly so in the comments RMSE, MSE or MAE is preferred while evaluating a linear regression model. So to answer your questions I will provide with the pros and cons of using RMSE and MAE and further more put forward some points to why R-squared is not preferred. 1. MAE vs. RMSE - Which metric is better ? Ideally this depends on ...


4

why the equality of both partial derivatives correspond to these hypothesis. I would rather understand when one partial derivative equals the other partial derivative multiplied by minus one. Your intuition of "trading off" by "subtracting" a value is correct when you speak in terms of $\Delta R$ and $\Delta P$ (as you yourself noticed in the edit), but ...


4

The explanation is simple, assume you have the following values: True Positives (TP) = 1 True Negatives (TN) = 998 False Positives (FP) = 1 False Negatives (FN) = 1 Accuracy = (TP + TN) / (TP + TN + FP + FN) = 999/1001 = 0.998 Precision = TP / (TP + FP) = 1/2 = 0.5 Recall = TP / (TP + FN) = 1/2 = 0.5 In summary you have an unbalanced dataset i.e. the ...


4

The coefficient of determination $r^2$ is defined in terms of variance: it is the proportion of variance in the dependent variable that is explained by the independent variable. Variance is a property of normal distributed data. Hence, the coefficient of determination can only be used when you assume that both the dependent and independent variables are ...


4

What you're looking for is something along the line of an ROC curve: Using the threshold as a decision parameter, you can observe the trade-off between FPR (False Positive Rate: how many of the articles not belonging to the author will be correctly classified) and TPR (True Positive Rate, aka recall: how many of the articles which are really by the author ...


4

This is in fact a very good question. The answer is simple, but depends on the case. In general, what we do after pushing a model to production we apply an audit process. Let me explain: in reality machine learning models that are being pushed to production are pushed to replace another process (e.g, manual process- this is the case of automation). At the ...


3

The Fı-score is preferred to simple classification accuracy in order to counter the problem of imbalanced datasets; if the thing you are looking for occurs only rarely anyway then a naive classifier can always say no and appear to be working very well! A variant on Fı is Fß, where Fß = (1+ß²) × [ (P × R) ÷ ( (ß² × P) + R ) ] Vary ...


3

If you can use python I suggest PyCM module. A vast variety of performance evaluation parameters is in access by this module and also you can use its documentation if you want to implement it by yourself. There is an example of it: >>> from pycm import * >>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2] # or y_actu = numpy.array([2, 0, 2, ...


3

TF Addons computes the F1 score and more generally the FBeta Score


3

Related concepts, but not the same. ROC-receiver operating curve AUC area under the curve Thank this post for explanation : Abbreviations AUC = Area Under the Curve. AUROC = Area Under the Receiver Operating Characteristic curve. AUC is used most of the time to mean AUROC, which is a bad practice since as Marc Claesen pointed out AUC is ambiguous (...


3

To select the most different rows, you would need to define first what you consider different. For ages and scores, subtracting values would work, for example: Row1 Age is 38 Score is 0.2 Row2 Age is 87 Score is 1.0 Difference by numeric feature: Age Diff is 49 Score Diff is 0.8 Those values could be normalized or weighted to account for different ...


3

The classes that define the columns/rows can be arbitrarily rearranged. Therefore, the "distance" of a misclassification to the diagonal has no meaning. So no, there is no such metric. I like @Dave's comment: "Is it worse to call a dog a cat than it is to call a dog a horse?" Maybe you'd ask yourself, "some classes feel closer ...


3

I give you that this is a weird way of displaying the data, but the accuracy is the only field that don't fit the schema. For example: precision recall f1-score support 0 0.84 0.97 0.90 160319 1 0.67 0.27 0.38 41010 As explained in How to interpret classification report of ...


3

The point of sample_weights is to give weights to specific sample (e.g. by their importance or certainty); not to specific classes. Apparently, the "balanced accuracy" is (from the user guide): the macro-average of recall scores per class So, since the score is averaged across classes - only the weights within class matters, not between classes.....


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