497
votes
Micro Average vs Macro average Performance in a Multiclass classification setting
Micro- and macro-averages (for whatever metric) will compute slightly different things, and thus their interpretation differs. A macro-average will compute the metric independently for each class and ...
- 5,079
69
votes
Accepted
When is precision more important over recall?
For rare cancer data modeling, anything that doesn't account for false-negatives is a crime. Recall is a better measure than precision.
For YouTube recommendations, false-negatives is less of a ...
- 3,610
62
votes
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What is the difference between bootstrapping and cross-validation?
Both cross validation and bootstrapping are resampling methods.
bootstrap resamples with replacement (and usually produces new "surrogate" data sets with the same number of cases as the original ...
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46
votes
Micro Average vs Macro average Performance in a Multiclass classification setting
This is the Original Post.
In Micro-average method, you sum up the individual true positives, false positives, and false negatives of the system for different sets and the apply them to get the ...
28
votes
Micro Average vs Macro average Performance in a Multiclass classification setting
In a multi-class setting micro-averaged precision and recall are always the same.
$$
P = \frac{\sum_c TP_c}{\sum_c TP_c + \sum_c FP_c}\\
R = \frac{\sum_c TP_c}{\sum_c TP_c + \sum_c FN_c}
$$
where c ...
- 470
28
votes
Accepted
Train/Test Split after performing SMOTE
When you use any sampling technique (specifically synthetic) you divide your data first and then apply synthetic sampling on the training data only. After you do the training, you use the test set (...
- 2,019
23
votes
Accepted
How many features to sample using Random Forests
I think in the original paper they suggest using $\log_2(N +1$), but either way the idea is the following:
The number of randomly selected features can influence the generalization error in two ways: ...
- 6,452
22
votes
How to define a custom performance metric in Keras?
You have to use Keras backend functions. Unfortunately they do not support the &-operator, so that you have to build a workaround: We generate matrices of the ...
- 381
22
votes
When is precision more important over recall?
I can give you my real case when recall is more important:
We have thousands of free customers registering in our website every week. The call center team wants to call them all, but it is impossible, ...
- 331
21
votes
Accepted
What are the disadvantages of accuracy?
A common complaint about accuracy is that it fails when the classes are imbalanced. For instance, if you get an accuracy of $98\%$, that sounds like a high $\text{A}$ in school, so you might be pretty ...
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18
votes
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Macro- or micro-average for imbalanced class problems
The choice of a metric depends on how you rank the importance of your classes and what you value from a classifier. Let's look at your example:
For example if we have a data set with 90%-10% class ...
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16
votes
Accepted
Micro-F1 and Macro-F1 are equal in binary classification and I don't know why
The difference between macro and micro averaging for performance metrics (such as the F1-score) is that macro weighs each class equally whereas micro weights each sample equally. If the distribution ...
- 311
12
votes
Accepted
Irregular Precision-Recall Curve
This is definitely possible. When you are reducing the threshold, you will never decrease the recall (you can only flag more of the positive examples as positive). Precision is looking at all the ...
- 9,398
12
votes
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 ...
- 4,088
11
votes
When is precision more important over recall?
Which is more important simply depends on what the costs of each error are.
Precision tends to involve direct costs; the more false positives you have, the more cost per true positive you have. If ...
- 301
11
votes
When is precision more important over recall?
I had a tough time remembering the difference between precision and recall, until I came up with this mnemonic for myself:
PREcision is to PREgnancy tests as reCALL is to CALL center.
With a ...
11
votes
Accepted
Is the F1 Score sensitive to the threshold?
Yes, it is. The formula:
$$F_1 = \frac{2TP}{2TP + FP + FN}$$
There is no symmetry. You have only $TP$ in the nominator which means that $F_1=0$ if $threshold=1$.
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10
votes
When is precision more important over recall?
Although in some situations recall may be more important than precision (or vice versa), you need both to get a more interpretable assessment.
For instance, as noted by @SmallChess, in the medical ...
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9
votes
Accepted
Difference between learning_curve and validation_curve
Both curves show the training and validation scores of an estimator on the y-axis.
A learning curve plots the score over varying numbers of training samples, while a validation curve plots the score ...
- 286
9
votes
Do I need validation data if my train and test accuracy/loss is consistent?
You don't always need 3 separate datasets. You usually split a dataset into 3 if you are doing some parameter or hyperparameter tuning before choosing a final model. Tuning will usually add bias from ...
- 2,059
9
votes
What are the disadvantages of accuracy?
In general, the main disadvantage of accuracy is that it masks the issue of class imbalance. For example if the data contains only 10% of positive instances, a majority baseline classifier which ...
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8
votes
Micro Average vs Macro average Performance in a Multiclass classification setting
Assume that we are classifying an email into one of the three groups: urgent, normal and spam. We compare the predicts with the ground truth labels, then we get the following confusion matrix and the ...
- 526
8
votes
Accepted
Micro Average vs Macro Average for Class Imbalance
The question is actually about understanding what it means to "take imbalance into account":
Micro-average "takes imbalance into account" in the sense that the resulting ...
- 25.9k
8
votes
precision@k and recall@k
A quick answer.
Note: Checking the references I could access fully, there are no discrepancies between the definitions as long as the terms are translated correctly.
Some definitions:
Relevant items: ...
- 2,413
7
votes
What is the difference between bootstrapping and cross-validation?
Bootstrapping is any test or metric that relies on random sampling with replacement.It is a method that helps in many situations like validation of a predictive model performance, ensemble methods, ...
6
votes
Accepted
Is Gini coefficient a good metric for measuring predictive model performance on highly imbalanced data
The Gini Coefficient can also be expressed in terms of the area under the ROC curve (AUC): G = 2*AUC -1 link. The ROC curve, on the other hand, is influenced by ...
- 6,452
6
votes
Micro Average vs Macro average Performance in a Multiclass classification setting
That's how it should be. I had the same result for my research. It seemed weird at first. But precision and recall should be the same while micro-averaging the result of multi-class single-label ...
6
votes
When is precision more important over recall?
Accumulation has a great answer on how you can come up with more examples explaining the importance of precision over recall and vice versa.
Most of the other answers make a compelling case for the ...
- 61
6
votes
Accepted
Can Micro-Average Roc Auc Score be larger than Class Roc Auc Scores
In a binary problem there's no reason to average the ROC (or any other metric). Normally micro and macro performances are used to obtain a single performance value based on individual binary ...
- 25.9k
6
votes
How to choose the right threshold for binary classification?
In short, you should be the judge of that: depending on the precision (interested to minimise "false alarms/FP") and recall (interested to minimise "missed positives/FN") you want ...
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