14
votes
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 ...
- 1,490
8
votes
Accepted
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 ...
- 251
7
votes
Accepted
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 ...
- 13.9k
6
votes
Accepted
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 ...
- 366
6
votes
Accepted
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 ...
- 18.8k
5
votes
Accepted
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 ...
- 9,348
5
votes
Accepted
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:
...
- 1,541
5
votes
Accepted
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 ...
- 6,532
5
votes
Accepted
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 ...
- 7,908
5
votes
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 ...
- 153
4
votes
Accepted
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 ...
- 1,789
4
votes
Accepted
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 ...
- 993
4
votes
Accepted
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
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4
votes
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 ...
- 141
4
votes
Accepted
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
$$
\text{cross-entropy} = -\frac{1}{n} \sum_{i=0}^{n} \sum_{j=0}^m \mathbf{y}_{ij} \log \hat{\mathbf{y}}_{ij}
$$...
- 3,920
4
votes
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.
...
- 1,954
4
votes
Accepted
What is the difference between maximum likelihood hypothesis and maximum a posteriori hypothesis?
I'll try and provide some intuition for you here, instead of focusing on the mechanics of the math behind the methods.
Imagine you are evaluating whether a coin is fair or not, so you collect a ...
- 376
4
votes
Accepted
Correct way of calculating probability
If my understanding is correct, your p_final_1 should give the correct result.
More simply:
...
- 25k
3
votes
Accepted
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{...
- 216
3
votes
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 ...
3
votes
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
...
- 2,382
3
votes
Accepted
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 ...
- 3,208
3
votes
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 ...
- 8,776
3
votes
How to convert an array of numbers into probability values?
This is a generalised question. There are lots of ways to normalise a given distribution. For example:
Normal distribution: You can physically inspect your function by graphing it against variables ...
- 793
3
votes
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 ...
- 1,041
3
votes
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 ...
- 6,595
3
votes
Accepted
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 ...
- 1,515
3
votes
Accepted
why we sample when predicting with Recurent Neural Network
It seems to me you have two questions:
Why use sampling to generate text from a trained RNN language model?
How does this particular sampling function from Keras work?
Why use sampling to generate ...
- 166
3
votes
Accepted
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 ...
- 1,899
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