Questions tagged [classification]
Statistical classification is the problem of identifying the sub-population to which new observations belong, where the identity of the sub-population is unknown, on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a variable behavior which can be studied by statistics.
7 questions from the last 7 days
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Building a Statistically Sound ML Model
Silent reader here in the statistics substack. One thing I've learned is that many "default" machine learning practices are being challenged due to fundamental statistical mistakes. This has ...
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How to compute confidence and uncertainity of model without ground truth from softmax output?
Suppose I have 3 classes A,B,C.
Performing:
y_pred = model.predict(X) # suppose X only two sampel
Returning vector with length ...
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How to assign the true classes in a column to proceed the analysis [closed]
I want to do a two way anova based on two independent varaibles question_type and string_lenght and one dependant variable response_time.
But i have this dataset where i have a problem in ...
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Comparing probabilities of two models
Consider a dataset and two binary classes CLASS_A and CLASS_B, with different proportions of 0 and 1. Suppose we train a model such as XGBClassifier for both ...
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Every regression problem, can be cast into classification problem. How about the vice versa? [closed]
If regression is about estimating the continuous value of ground truth and predicted values, and continuous values can be cast into discrete values, then discrete values can be labeled as ...
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Develop credit rating based on binary variable (in R preferably)
I work with credit data in R. I have a loan dataset where I have borrower and credit specific variables and a binary indicator default/notdefault. At first step I do logit and get probability of ...
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XGBoost F1 score impovement methods for multi class classification [duplicate]
I am building a multiclass classification (5 classes) using XGBoost. Currently using 56 features for 1.6 million customer base having balanced classes.
The overall accuracy is 83%, F1 score is 0.81, ...
