# Tag Info

### What is the relationship between the accuracy and the loss in deep learning?

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 ...
• 972
Accepted

### What's the difference between Sklearn F1 score 'micro' and 'weighted' for a multi class classification problem?

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 ...
• 1,792
Accepted

### How to interpret classification report of scikit-learn?

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 ...
• 441
Accepted

### Balanced Accuracy vs. F1 Score

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 ...
• 803

### Why do we use a Gaussian kernel as a similarity metric?

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 ...
• 3,470

### What is the relationship between the accuracy and the loss in deep learning?

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 ...
• 497

### MAD vs RMSE vs MAE vs MSLE vs R²: When to use which?

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 ...
• 231
Accepted

### What is Continuous Ranked Probability Score (CRPS)?

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 ...
• 2,786

### What is the relationship between the accuracy and the loss in deep learning?

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 ...
• 181
Accepted

### Cosine similarity vs The Levenshtein distance

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 ...
• 24.4k

### Can the F1 score be equal to zero?

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.
Accepted

### Interpretability of RMSE and R squared scores on cross validation

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 ...
• 1,714
Accepted

### F1_score(average='micro') is equal to calculating accuracy for multiclasification

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/...

### Log loss vs accuracy for deciding between different learning rates?

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 ...
• 1,234