Questions tagged [metric]

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15 views

Binary classifier on imbalanced dataset yields weird PR curve

I have a dataset with ~6M points, 9 features and two classes. The minority class represents just under 2% of the data. The data is first divided into 100 batches and a different classifier is trained ...
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1answer
15 views

Choice of f1 score for highly imbalanced dataset?

I am confused whether to use f1 score with 'micro' average or 'macro' average for better evaluation. Given my dataset is highly imbalanced(600:100000)
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1answer
23 views

Ball Tree and Pseudometrics

The docs for sklearn.neighbors.DistanceMetric state that in order to be used within the BallTree, the distance must be a true metric (i.e. be non-negative, 0 ...
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1answer
9 views

Appropriate model metric for a truncated response variable?

Here's a straightforward question I can't seem to find a good answer to. Let's say you're using some variables to predict age. I'm assuming a regression model is the right approach. In this case, what ...
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18 views

GridSearch on imbalanced multi-class dataset

I have an imbalanced multi-class dataset (GTSRB) and would like to use GridSearch to determine the hyperparameters for an SVM. As metric for the evaluation I chose F1 with average macro. ...
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1answer
128 views

Interpretability of RMSE and R squared scores on cross validation

I'm working on a regression problem with 30k rows in my dataset, decided to use XGBoost mainly to avoid processing data for a quick primitive model. And i noticed upon doing cross-validation that ...
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183 views

Finding linear transformation under which distance matrices are similar

I have n sets of vectors, where each set S_i contains k vectors in ...
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0answers
8 views

What is Bit Per Character?

What is Bits per Character (bpc) metric which has been used to measure the model accuracy with reference to text8 and ...
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1answer
11 views

what is the representation/meaning/implication in real life of $P(\text{+})$ in the wiki Drug testing Example about Bayes' theorem

In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule) describes the probability of an event, based on prior knowledge of conditions that might be related to ...
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32 views

What's the difference between these two custom sparse categorical accuracy functions?

I have a sequence classification model featuring CustomELMo Embeddings layer + BiLSTM + Fully Connected layer. I've found two custom metrics for sparse_categorical_accuracy, but can't wrap my head ...
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11 views

How to optimize MAPE in regression algorithms

I have a regression task where the label is varying from about 0.001 to 1000. One of the feature called group, for example, group A corresponding label from 0-0.1 and group G corresponding label from ...
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9 views

Loss function for multi-class classifiction where output variable is a level i.e the various classes are dependent on each other

Let's say we are classifying Images of cat , fish and human. Classifying a cat as human is as wrong as classifying it as fish, so here the normal loss functions/ metrics like Confusion matrix is fine. ...
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1answer
16 views

How is the “loss” calculated which is supplied by the callback log in Keras?

I.e. categorical cross entropy? binary cross entropy? Something else? Or is it perhaps the loss function which you pass into the model.compile method?
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1answer
23 views

Metrics for Name Entity Recognition

Working on a NER project, I have been facing the problem of evaluating my model during training. I cannot be using the accuracy metrics or f1 score or any other metrics to evaluate my model on runtime ...
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22 views

For a multiclass classification problem, how do we find the cohen kappa score?

So I have a multiclass classification problem and I have found the Matthews Correlation Coefficient of that (https://scikit-learn.org/stable/modules/model_evaluation.html#matthews-correlation-...
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1answer
23 views

How is computed the train data set score and why?

In a lot of machine learning blogs or review, the training dataset accuracy (or other metric) is given alongside the test dataset score. Is this score calculated through the training, or is the ...
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2answers
20 views

I am getting different mean_absolute_error when i retrain my model everything same

I have set my numpy random seed to 0. I am training on colab and using keras. I didn't change anything. I just re-ran my cell and the val_absolute_error changed. Code: ...
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1answer
47 views

Best metric in imbalanced classification for multi-label classification

My test data are imbalanced, i tried to use the precision or the gmean as metrics for a multi-label learning model, but both metrics are not very informative. Is there any way to use for example the ...
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9 views

Manual setting of target variable based on features' minimum values: f1 score = 1

I am building a classifier for user engagement in my website. Basically, since there are no "proxy" for engagement, i.e. there is no pre-defined target variable, I came up with minimum thresholds ...
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23 views

What is divergence exactly in machine learning?

I know about KL divergence, JS Divergence and clearly know that it is different from the divergence in calculus. I have an intutive feeling of divergence as it roughly compares the closeness of two ...
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1answer
46 views

How to interpret metrics of a model after scaling the data

I have a GradientBoostingRegressor from scikit-learn which I trained. Afterwards, I obviously would like to know how good the model is. So, on a non-scaled dataset ...
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25 views

I got a overall mean average precision score of 0 for a recommendation engine

I just wanted to know if receiving an overall MAP score of 0 in a recommendation engine was possible, or a sign that my calculation or my logic for the engine was wrong.
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1answer
44 views

Metric (rather than RMSE, MSE, etc.) to choose the best model in terms of the ability to detect peaks better

I have created multiple regression models and wanted to choose the best one. One common metric would be RMSE, as you know. When I looked at the results, second model (RMSE = 0.15) was better able to ...
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10 views

How to measure the success of a seasonal product or what should be the northstar KPI

Not sure if this is the right forum to post this question. This is more of a product management question, I searched all the communities in StackExchange and thought this might be the closest to ...
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0answers
40 views

Is there a metric for counting AND classification simultaneously?

I'm working on a project that mixes object detection and crowd counting. The metric for object detection is mAP, which combines the regression of the bounding boxes with the precision of the ...
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2answers
208 views

How to compute f1 in tensorflow

I have code that computes the accuracy, but now I would like to compute the f1 score. ...
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3answers
100 views

How to select between models when AUC scores are similar?

I use two machine learning algorithms for binary classification and I get this result : Algo 1 : ...
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12 views

Metrics for stain normalization

Are there any metrics or methods for assessing stain normalization techniques?
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0answers
59 views

What does the ratio of precision/recall to their ideal values mean, and why are they equal?

When evaluating rankings, Normalized Discounted Cumulative Gain (NDCG) normalizes the score to a [0,1] range by dividing by the ideal score. What happens if we take the same idea to Precision (Pre) ...
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0answers
100 views

Using logLoss as metric function for highly unbalanced dataset

ihave an highly unbalanced dataset and the caret pacjage only allows me to select accuracy or kappa as performance metric. Is it correct to use a mlogloss function to compute model performance? Do you ...
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0answers
36 views

How to Monitor ML Classification Models in production?

I've often heard of measures like Population Stability Index and Characteristic Stability Index. I might be mistaken, but these seem to be more applicable towards looking at the changes in univariate ...
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0answers
73 views

how to interpret a high AUC value but a low F1 score after upsampling minority class?

I use to gradient boosting to classify my data between default and paid. The data is very imbalanced where default is in minority. The fisrt classification report from sklearn gradient boosting ...
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1answer
253 views

Can macro F1 score be greater than micro F1 score?

I am reading about evaluation metrics, and it seems that micro scores are more useful. But I was wondering about scenarios where macro F1 score is greater than micro F1 score, and if this is at all ...
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2answers
22 views

How should multiclass classifier performance be measured when one type of error is preferred over another?

Sorry if this question has been asked before--I am having trouble searching this topic since I'm not sure of my wording. Say you have a classification problem where there are more than two labels ...
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1answer
142 views

Metrics for presenting RNN/LSTM result

I am working on a two different architecture based on LSTM model to predict the users next action based on the previous actions. I am wondering, what is the best way to present the result? Is it okay ...
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3answers
47 views

A metric between trees

I have certain tree structures. I am not an expert in machine learning. As I would with take KNN, I would calculate distances via metric function and a new data point and the points from the training ...
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2answers
34 views

How to measure accuracy of a route prediction

I developed a new route prediction algorithm and I am trying to find a metric that informs on how well a prediction was. This metric is meant to be used offline, meaning that the goal is not to ...
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2answers
998 views

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

In regression problems, you can use various different metrics to check how well your model is doing: Mean Absolute Deviation (MAD): In $[0, \infty)$, the smaller the better Root Mean Squared Error (...
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3answers
867 views

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

I have created three different models using deep learning for multi-class classification and each model gave me a different accuracy and loss value. The results of the testing model as the following: ...
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1answer
49 views

Understanding the Gini/AUC metric as out-of-development performance metric

Assume we develop a model for a binary classification task that reaches a certain Gini/AUROC estimate on the validation ( or training ) sample, among others. This is an overall good metric, often used ...
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0answers
24 views

Dimension of the manifold on which my data sits

Suppose that I have data points, in the form of vectors with binary entries. We create a metric space, or Vietoris-Rips complex, using the Hamming distance between the data points. I would like to ...
4
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1answer
218 views

issue with early-stopping on f1 score with imbalanced data

I have a highly imbalanced dataset with less than 0.5% of the minor class. Using Keras, I'm training DNN on the training set and evaluate performance on validation set. Loss function is ...
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1answer
3k views

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

I have a multi-class classification problem with class imbalance. I search the best metric to evaluate my model. Sklearn has multiple way of calculating F1 score. I would like to understand the ...
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1answer
113 views

How do you measure performance for word prediction tasks?

Say I have to predict the next word in a sentence, given the initial few words. Suppose the prefix is "I went to _____". This prefix is common enough that it might appear 10 times in the training ...
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2answers
239 views

Can Precision-Recall be improved for imbalanced sample?

I tried out a few models on a highly imbalanced sample (~2:100) where I can get decent AUC from ROC (test sample). But when I plot precision-recall (test sample), it looks horrible. Kind of like the ...
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4answers
261 views

Log loss vs accuracy for deciding between different learning rates?

While model tuning using cross validation and grid search I was plotting the graph of different learning rate against log loss and accuracy separately. Log loss When I used log loss as score in ...
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0answers
44 views

Decent ROC, but horrible Precision-Recall curve

I was working on a model with following process: Split to training/validation/test sets Try a series of different models like GBM, RF, Logistic Regressions Optimize hyper-params on them using ...
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1answer
1k views

Semantic segmentation: mean IOU in presence of missing classes

It seems to me that the mean IOU is a poor metric in the presence of unbalanced classes. E.g., suppose I have 10 classes but one image has only 2 classes present in its label. Consider the prediction ...
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0answers
68 views

Why is the area under the precision-recall curve not used as scoring function more often?

When I am training and evaluating classifiers or hyperparameter tuning I don't like to look at precision and recall metrics alone because those numbers depend on a threshold that I will set afterwards ...
2
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1answer
83 views

What's the best metric for evaluate an estimator for a multi class problem with class imbalance dataset?

accuracy, precision, f1, ROC are good for binary single class problem. but for more complex problem (imbalance multi-class problem), what should i use? Do you have any recommendation?