Questions tagged [naive-bayes]
A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem with strong independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model".
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Why is $p(y|x)$ infeasible when discussing Naive Bayes?
This is a question in which I think I am missing some key information. When discussing Naive Bayes, I've noticed that lecturers typically say that we really want is $p(y|x)$ (label given features), ...
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Which Naive Bayes Classifier fits best for categorical data? [closed]
Which Naive Bayes Classifier would you choose to fit data with two categorical variables and a class variable?
weather=['Sunny','Sunny','Overcast','Rainy','Rainy','Rainy','Overcast','Sunny','Sunny', ...
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Bayes and Naive Bayes code implementations
I know that Bayes classifier assigns the new data point $\pmb{x}$ to the class $\omega_j, \ j=1,\dots,M$, when
$p(\omega_j \mid \pmb{x}) = \max_{q=1,\dots,M}p(\omega_q \mid \pmb{x})$,
where
$p(\...
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How normal is the following distribution of data? [closed]
I'm using the following dataset with 2 columns (features) and 1 label to train a Gaussian Naive Bayes classifier. How would you determine (using a stastiscal normality test) whether the data is ...
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Tuning for Naive Bayes
If you are tuning a Naive Bayes model using caret, can someone explain how increasing or decreasing the Laplace smoother and bandwidth impact the results?
I ...
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Difference between a Bayes classifier with diagonal multivariate gaussian class conditionals and a Naive Bayes classifier?
In a Bayes classifier, let's say we want to fit a multivariate Gaussian distribution for the class-conditional probabilities and we restrict its covariance matrix to be a diagonal matrix.
In a Naive ...
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Accuracy, Sensitivity, Specificity, & ROC AUC [duplicate]
In the context of predictive modeling, when comparing clasification models, What statistic should be considered more important over the others: Accuracy, sensitivity, specificity, or area under ROC ...
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Maximum likelihood: Bernoulli
I would really appreciate if anyone you can explain how it went from step 1 to the answer provided below. This is from the book Doing Data Science by Cathy O'Neil and Rachel Schutt pages 101 to 102.
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How to use densities for Naive Bayesian classification?
This is a simple question I think.
I am using the following R package for non parametric naive Bayesian classification:
https://www.google.co.uk/amp/s/rdrr.io/a/cran/naivebayes/man/...
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Naive Bayes for Categorical Features (Non Binary)
How do i use Naive Bayes Classifier (Using sklearn) for a Dataset
considering that my feature set is categorical, ie more than 2 categories per feature are present.
I've looked everywhere, some ...
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Computing Gaussian Naive Bayes
I am not more of a mathematics person, I am trying to learn but I could not break the math down.
Here is the data set I am using
I wanted to use Gaussian Naive Bayes to find our whether the gender ...
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R: Naive Bayes model e1071, why does it works with totally different columns in training ad testing?
I'm working with the e1071:: naiveBayes() function, but I don't figure out how it works. My doubts arose when I read this post. I've posted this question on SO, but ...
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Mixture of Multinomials
I have implemented a Naive Bayes Model for a mixture of independent Bernoulli. Where the conditional probability can be written as:
$\mathbb{P}(Y=j | X) \propto \omega_{j} \prod_{i=1}^{d} \mu_{i, j}^{...
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Conditional Probability Given Multiple Priors
Reference: ilanman Oct 3, '16 at 13:27. Even though years old, this discussion is relevant to my current interests. The P(Ai|B) in the last formula above, reverses the position of the posterior and ...
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Bayesian repeated updates, likelihood functions with different nature
Let's say we have a prior probability of some diseases 'D'. Then we have some data and likelihood function of symptoms (S) P(S|D) and we update priors. Then we have age (A) likelihood function P(A|D) ...
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In-depth explanation of the multinomial Bayes classifier
I am new to machine learning and am trying to understand the different classifiers. I have searched the internet and books for a comprehensive explanation of the Multinomial Bayes classifier, but I ...
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Decision boundary for categorical Bayesian network
I know that categorical Naive Bayes (categorical predictors, binary target) has a linear classification boundary. I'm wondering what the decision boundary for an arbitrary categorical Bayesian network ...
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Updating a probability with additional knowledge. Bayes Theorem
I am quite confused with using Bayes theorem for the following problem. And I am not sure it can be applied at all.
I have a football website data with user views. Each view corresponds to a specific ...
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Size of training in Naive Bayes
I just started getting involved with Machine Learning and I decided to create a spam filter for my social app, using the Naive Bayes classifier. I'm following this guide: https://hackernoon.com/how-to-...
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What is to be done when PDFs are not Gaussian/Normal in Naive Bayes Classifier
While analyzing the data for a given problem set, I came across a few distributions which are not Gaussian in nature. They are not even uniform or Gamma distributions(so that I can write a function, ...
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Jurafsky and Martin (2018) Do not understand formula in naiye bayes classifier
Currently I am reading Language and Speech Processing by , Chapter 4 Naiye Bayes and Sentiment Classification.
At page $7,$ when the authors discuss worked example.
Data set is as follows:
Training ...
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What are the relationships among Markov Property, Stationarity, and Time Invariance
I am wondering if there is or are any relationship among those. I have understood Markov Property by reading Wikipedia, but it is still confusing to figure out if there is any relationship among those ...
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Can anyone help to explain one of the variables in a figure that illustrates how posterior probabilities shift and move around?
I am learning this post.
The book gives this figure to illustrate how posterior probabilities shift and move around
Here is the code
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Which distribution should I use for Naive Bayes algorithm(Gaussian or Rayleigh)? What to do with categorical data?
I am predicting whether credit card application of an individual would be approved or not given his/her credentials.
I have the following dataset:
The variable descriptions are as follows:
I need ...
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What is the meaning of generating data from a probabilistic model such as a naive bayes classifier?
I am studying probabilistic modeling but I am stuck with the concept of generating data from the probabilistic model. Say I have built a naive bayes classification model, what is the point of ...
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Hoes does laplace smoothing in Naive Bayes control high bias and high variance?
I'm trying to understand how laplace smoothing exactly helps to balance between overfitting and underfitting.
I know that Laplace smoothing is used as a fail safe probability if there's a any ...
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Bayesian Formula for multiple events
I know that Bayesian Formula for A giving B is like this
$ p(A | B) = \frac{p(B|A) p(A)}{p(B)}$
In case there are multiple events B C D What will the equation be like in the simplest form of a ...
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To say my model is a stochastic model,what assumptions do I need to make?
I am trying to understand what a stochastic model is and assumptions to be able to say my model is a stochastic model. I am new to it, so I may confuse you.
I have gone through Markov chain, Markov ...
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How to determine the discount percentage of a product for a given product category and brand?
We are performing the analysis of data of an online shopping site.
Please refer to the dataset mentioned in this link
The fields of the dataset are:
We have been asked to do the following:
Perform ...
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Is Chow Liu's scoring algorithm to have at most one root node?
I am told that Chow Liu's algorithm can have at most one root node. In the fisr place what does it mean? I am wondering how I can apply Chow Liu's scoring function for more than one root node to do a ...
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What is the difference between Bayesian Network and Dynamic Bayesian Network?
I just got the sentences below from a web site while studying Bayesian Network:
"A dynamic Bayesian network (DBN) is a Bayesian network extended with additional mechanisms that are capable of ...
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In Naive Bayes classifier how is P(sneezing,builder|flu) = P(sneezing|flu)P(builder|flu)?
Please refer to this literature:
According to Naive Bayes classification algorithm:
$P(\text{sneezing},\text{builder}\mid\text{flu}) = P(\text{sneezing}\mid\text{flu})P(\text{builder}\mid\text{flu}) ...
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Why can logistic regression always outperform naive Bayes for every conditional distribution?
We know that the generative model assumes that $X_i \perp X_{-i}| Y$; while the discriminative model assumes that $p(Y=1|x; \alpha)=\frac{e^{\alpha_0+\sum_{i=1}^n\alpha_ix_i}}{1+e^{\alpha_0+\sum_{i=1}^...
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Naive Bayes linearity [duplicate]
By theory I know that Naive Bayes Classifier is a linear one, but when I implemented the decision boundary it was a curve (not linear as shown below). Is there any explanation why this is happening? ...
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How valid is this Stacking Model (input features to weak learners are different)?
I have a set of features with 6 of them being categorical, 1 continuous and 2 textual in type.
I have to predict the labels ( 10 in number) for them.
I tried applying several models and came to a ...
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Counting frequencies in Multinomial Naive Bayes
I have noticed some ambiguity/inconsistency in how various authors calculate the p(word|label) estimate in Multinomial Naive Bayes. In some cases, this value is calculated by counting words without ...
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Derivation of the formula for the probability of a class, given conditionally independent attributes
The following is a formula that finds the posterior probability of a class (i.e. yes or no) given four conditionally independent attributes:
$$P(c|X) = P(x_1|c)\cdot P(x_2|c)\cdot P(x_3|c)\cdot P(x_4|...
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Calculating priors with large number of classes
I'm trying to classify users into ~1K different groups. I'm trying to build a MAP classifier and have estimated my prior and posterior distributions using large amounts of data. The issue that I've ...
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Gaussian Naive Bayes Classifier
There are three nice Bayes classifier techniques: Bernoulli, Multinomial, and Gaussian
If we have a dataset whose samples have continuous-valued features, then Gaussian Bayes Classifier is used.
In ...
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Determining the decision boundary for Naive Bayes
I'd like to know if this is a sensible idea and if there exist any already formed methods to do this (I'm new to the data science area).
Essentially, I have used Naive Bayes to accurately classify ...
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manual implementation of Gaussian naive bayesian returns posterior larger than 1
I try to implement Gaussian naive bayesian manually in R. I test my model on iris data set. I would like to build a predictive model. That is, I would like to ...
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Naive bayes computation of denominator
I'm wondering about the denominator in this computation :
...
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Naive Bayes missclassification rate across classes
I have a dataset with income, age sex and education as categorical features. I used R to create a Naive Bayes classifier as follows: income ~ age + sex + education.
I got the following a-priori and ...
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Why is it easier to incorporate arbitrary features into discriminative models?
It is often stated, that when arbitrary features are implied, generative models (e.g. Naive Bayes) are a lesser fit than discriminative ones, mainly for being harder to build. How would you elucidate ...
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Classification with ONLY categorical data
Suppose I have a table with some factor characteristics of some plants. For instance, petal color, pollen color, and so on. What is the best way to classify that data? Is it feasible to use some of ...
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Are there useful applications for Bayes Nets (vs. Naive Bayes)?
I am trying to learn about Bayesian networks and try to make them work in the context of a simple prediction problem. But my question is more theoretical:
For argument's sake, assume we have a ...
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How to make a prediction with Bayes Classifier after computing MLE?
I'm trying to figure out the role of computing the MLE for classification/prediction purposes with the Bayes Classifier.
Let's say I'm given a set of data assumed to be Gaussian. I can then compute ...
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How to validate classification model with ordinal information
I have a Naive Bayes model that predicts 3 classes. As you increase each class it means that the condition is more severe. 0 means no condition, 1 is concern and 2 is that they have the condition. I ...
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Why do the posterior probabilities violate the axioms of probability when we apply Bayesian update without likelihood computation?
Suppose that the unknown parameter $\Theta$ is Bernoulli and we make $n$ observations $X_1,X_2,\ldots,X_n$, which are continuous random variables. Assuming that $X_1,X_2,\ldots,X_n$ are conditionally ...
