# Tag Info

Accepted

### Are the raw probabilities obtained from XGBoost, representative of the true underlying probabilties?

It depends on the definition of accurate model, but in general the answer to your question 1) is No. Regarding your second question (based on results in the paper of Niculescu-Mizil & Caruana ...
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### How does binary cross entropy work?

When doing logistic regression you start calculating a bunch of probabilities $p_i$ and your target is maximize the product of those probabilities (as they're considered independent events). The ...
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### Why does the naive bayes algorithm make the naive assumption that features are independent to each other?

By doing so, the joint distribution can be found easily by just multiplying the probability of each feature whilst in the real world they may not be independent and you have to find the correct joint ...
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### What is difference between Bayesian Networks and Belief Networks?

Both are literally the same. A Belief network is the one, where we establish a belief that certain event A will occur, given B. The network assumes the structure of a directed graph. The term Bayesian ...
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### What is "noise" in observed data?

When you have sensors, the values you receive change even if the signal that was recorded didn't change. This is one example of noise. When you have a model of the world, it abstracts from the real ...
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### Confidence intervals for binary classification probabilities

I don't think there is a good way to do this for all models, however for a lot of models it's possible to get a sense of uncertainty (this is the keyword you are looking for) in your predictions. I'll ...
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### Compute parameters of a PDF (probability density function) for which no closed form expression is available

Your problem could be solved either by direct numeric integration or by MCMC. Numeric integration can be performed most easily by scipy: ...
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### How to use different classes of words in CountVectorizer()

First of all your question is about stemming words as mentioned in the other answer which can be found in any Python NLP library such as Spacy or NLTK. The other point to mention here is that despite ...
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### Classification problem approach with Python

You are looking at a Classification problem. Logistic regression, Decision trees, SVM. Any of the above can solve the job for you. But selecting the best model depends upon how good it is able to ...
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### What does a predicted probability really mean, without considering the accuracy of the underlying model?

Accuracy is measured in classification model by comparing the predicted labels to the actual known labels. The predicted labels are a function of both the predicted probabilities for each class and a ...
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### Testing fit of probability distribution

Use chi-square test to check the goodness of fit to a specific distribution http://courses.wcupa.edu/rbove/Berenson/10th%20ed%20CD-ROM%20topics/section12_5.pdf

### How to convert an array of numbers into probability values?

Any Survival Function (1 minus the CDF) will have the desired property. Exponential is a potentially good candidate here, as it sometimes can be used to describe distances, but it's hard to say ...
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### Data-generating probability distribution, probability distribution of a dataset, in ML

It's more of a theoretical distribution, that a concrete one. The main idea is this: we consider all data to have an underlying distribution which generates the data. Through the procedure of creating ...
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### Pytorch doing a cross entropy loss when the predictions already have probabilities

You can implement categorical cross entropy pretty easily yourself. It is calculated as $$\text{cross-entropy} = -\frac{1}{n} \sum_{i=0}^{n} \sum_{j=0}^m \mathbf{y}_{ij} \log \hat{\mathbf{y}}_{ij}$$...

### What is the meaning of likelihood?

All of the answers here, including the accepted one, are conspicuously confused. I down-voted the accepted answer but downvotes of users who lack reputation in this "community" are not counted. I have ...

### why naive is needed in Naive Bayes ,what happens if naive is not included in Bayes theorem?

I do not think your formulation is correct. What you have described are just conditional distributions for each word in the sentence but not the joint conditional distribution, given a specific class. ...
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### Correct way of calculating probability

If my understanding is correct, your p_final_1 should give the correct result. More simply: ...
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### In supervised learning, what does "Estimating $p(y \vert x)$" mean?

You are correct that \begin{equation*} p(y|\mathbf{x})=\dfrac{p(\mathbf{x},y)}{p(\mathbf{x})}. \end{equation*} Similarly, we can write the joint probability $p(\mathbf{x},y)$ as follows: \begin{...

### Xgboost predict probabilities

Curious Georg if you ran across this article in your pursuit of trying to generate probabilities. It is worth noting that binary:logistic and ...

### Ensemble Probabilities of the different models

I think it can be done by using this command at the time of prediction, giving example in R ...
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### Very low probability in naive Bayes classifier

I can't tell you for sure without you describing your calculation more or showing code, but my guess is you're not actually calculating the posterior probability here. I bet this is just the ...

### Find-out abnormal behavior over the time

Your problem definition You have time series data which is used to measure the pressure using your sensor. You wish to identify when the pressure recordings are abnormal. This problem would be best ...

### Confidence value for face recognition

If I had to calculate such a function, I would: Calculate the probability (not Z-score) for $x$ in a two-tailed test (https://en.wikipedia.org/wiki/One-_and_two-tailed_tests) for the probability that ...

### XGBoost: How to set the probability threshold for multi class classification

You don't set it in xgboost. Its job is to return probabilities in predict_proba. predict does the logical thing and tells you ...
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### Convolution and Pooling as Infinitely strong priors

A prior distribution expresses your assumptions about the model without observing any data. E.g. when doing linear regression, you a priori assume that the slope is close to zero. Now you start ...
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### Understanding output probabilites of xgboost in multiclass problems

What you were told is a worst case scenario. With 5 labels, 20.01% is the lowest possible value that a model would need to choose one class over the other. If the probability for each of the 5 classes ...
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### How to model user choice probability: binary model vs multi class model

This is a textbook multi-armed bandit problem where Morpheus needs to learn the correct policy about offering pills. As you’ve said the Neos are independent, and making the assumption that there is a ...
My understanding is: The first line is OK - it derives from Bayes Rule Assume that this probability follows a logistic function, that is that $$P(C_1|x) = \frac{1}{1+exp(-a)}$$ Then if  y = \...