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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Could someone guide me on how to obtain the general result of the percentage if the Principal components belong mostly to an open-eyed person?

Good morning. Excuse me, I'm asking for advice on the following problem: I generate the following Principal Components. Principal components labeled 1 belong to the alpha waves of an open-eyed person ...
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How to prepare the training data for Support Vector Machine?

I'm currently doing some comparison of Naive Bayes Algorithm and Support Vector Machine classifying news to see each algorithm's accuracy. I already know how to prepare the training data for Naive ...
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What goes wrong in linear regression if we assume a Naive Bayes model when features aren't necessarily independent?

In the notes I'm working through, it says that in low dimensional models, it's often the case that we cannot get away with assuming that features are uncorrelated/independent i.e. that we can't use ...
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Bayes Classifier example: is my work right? What does it mean?

I have this dataset and I am learning about Bayes Classifier. After data cleaning, I have tried to use Bayes classifier on it. I used R with this code: ...
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Reduce dimensionality and classify EEG signals

Good morning, I am new to machine learning, if someone could recommend a book to reduce dimensions (PCA), and classify (Naive Bayes), the purpose is to classify EEG signals, I have already applied pre-...
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Naive Question: Naive Bayes vs Common Sense

Do you know the classical problem of sunny / rainy / overcast days and output yes / no the game will played. See the image below. Now the problem question is "Players will play if weather is sunny. Is ...
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Naive Bayes : intuition that resampling (down/up/SMOTE/ROSE) affects prior probabilities in a wrong way

I have a supervised classification problem with unbalanced class to predict (Event = 1/100 Non Event). I have the intuition that using resampling methods such as down/up/SMOTE/ROSE with Naive Bayes ...
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Classification Models! How to chose? [duplicate]

I am trying to transition into the Data Science field and and very curious about getting as much practical and theoretical knowledge as possible. Is there any resources that can help with identifying ...
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How to combine Naive Bayes and Logistic Regression?

I am working on an NLP binary classification problem. I trained a model with linear regression and a naive Bayes classifier. I found out that I am getting a good recall value in linear regression but ...
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What advantages does Naive Bayes have over the “not naive” Bayes?

Repeating the question What advantage does Naive Bayes have over "not naive" Bayes? Considering the fact that the assumption about conditional independence is often violated, why do we make it? As ...
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Why is MLE used in Naive Bayes to estimate parameters if it's a frequentist approach?

Something that confuses me a bit. I thought, MLE was a frequentist method. But as far as I understand, wen can estimate the parameters of Naive Bayes using MLE. How come MLE is used in a Bayesian ...
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How can I replicate the process sklearn calculates the posterior probabilities?

I have a question pertaining to scikit-learn methods. Can I get the same probabilities obtained with predict_log_proba() by hand calculating the likelihoods and prior obtained with feature_log_prob_ ...
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How to distinguish Multivariate Bernoulli Distribution from Binomial Distribution,Multinoulli distribution,Multinomial distribution?

Ok While studying naive Bayes I came across this question and from the accepted answer I reach to this blog. While reading this blog I got a clear idea of how Bernoulli distribution turns to (...
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Correcting a naive Bayes classifier based on the performance on the training dataset

I'm sorry, this seems like something that's already been discussed to death, but I don't seem to be able to phrase the question right to find the answer. Say I have a naive Bayes spam filter trained ...
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Why to use log while calculating probability of an email being spam?

I worked on a basic spam email project (Naive Bayes classifier with Laplace smoothing). In source code, to calculate probabilities of spam or ham, log of the final result is being used. Why is it ...
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Making naïve Bayes less naïve by accounting for mutual dependence

I'm playing with the pet example of figuring out the probability someone has the flu given certain symptoms, namely fever and nausea. Let's define our priors, considering the "probability of x" as ...
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How is Laplace Smoothing used in this example of Binary classification in Naive Bayes

I am following CS229 course by Andrew Ng. On this lecture note it talks about using Laplace smoothing to bypass situations of 0-probabilities. What does not make sense is the immediate jump to the ...
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What is a good accuracy for Naive Bayes Classifier?

I'm currently train the Naive Bayes Classifier in TextBlob for my Sentiment Analysis. Before I used the training I had many positive or negative sentences that were determined as neutral sentences, so ...
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Bayes Classification vs Naive Bayes Classification

Generally, known that Bayes Classifier is optimal for the probability of error. But when I did some experiments: First Case: I have 2 classes data and their covariance matrices correlated in this ...
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Multinomial Likelihood function with conditional probabilities drawn from Gaussian Mixtures

I have a Likelihood function that is a multinomial distribution: $p(X | \alpha, \beta) = \prod_{n=1}^N [p(x_n | \alpha)]^{I_n} [p(x_n | \beta)]^{1-I_n}$ where $I_n$ is an indicator function and both $...
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Language Identification Better Results with Unigrams

I have a school project which consists of identifying each language of a tweet from a dataset of tweets. The dataset contains tweets in Spanish, Portuguese, English, Basque, Galician and Catalan. The ...
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Should I use Gaussian naive Bayes or Bernoulli naive Bayes?

My data set looks like following: I have 5 continuous columns in the data set: income, age, experience, money spent/month, and mortgage. I have 5 categorical columns: all categorical (1's, 0's and 2'...
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Document classification with Naive Bayes performs worse than other methods

I am doing a document classification challenge on hackerrank.com. The training data is $X$ strings classified into $8$ classes. My approach is to use word frequencies and naive bayes (...
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Figuring confidence intervals for classification using the variance of categorical random variables

Pronk et al show how to calculate confidence intervals for Bayesian classifications. A key part of their model is the variance of the ratio of two binomially distributed random variables (13). They ...
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Doubt in bayes classifier error calculation

I have recently started machine learning on my own. I started reading Duda art and start book. That author says that Bayes classifier has a min error. He calculates $$\begin{equation} P(error|x)=\...
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Naive Bayes Assumption formal definition

I am reading Andrew Ng's lecture notes available at http://cs229.stanford.edu/notes/cs229-notes2.pdf on Naive Bayes assumption. He writes For instance, if y = 1 ...
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Bayesian AB Testing: Simulated vs analytically derived posterior

Consider the problem of distributing two leaflets A and B to increase sales of a given product. We distribute 20 of leaflet A (with 8 successes) and 20 of leaflet B (with 3 successes). We know want to ...
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Classification ML for sports betting

I'm trying to model the problem on my own and I just want to have feedback if I'm on the right track. Suppose I want to build a model that outputs the decision rule for the outcomes of a football (...
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Improve F1-score for multiclass text classification with highly imbalanced dataset

I am trying to classify clients' complaints with a dataset of 180k complaints. I have 132 classes like this: Counter({'DIAG_000_NODIAG': 66291, 'FORWARD': 29126, 'DIAG_087': 22843, 'DIAG_049': 17668, ...
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How do I calculate a Maximum Likelihood Parameter Estimate for this binary data - Naive Bayes

I find I best learn by example but I can't seem to find any that match with this, or at most, people appear bizarrely unwilling to show where in these abstract equations you actually insert which ...
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what is the difference between Naive Bayes and NON-Naive Bayes?

In Naive Bayes Why is it necessary for Naive to assumes that the input features are independent and not co-related . can anyone explain with a very simple example on what is the problem of events ...
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Conditional independence of attributes in NB algorithm and independence of levels in Target Encoding

This is not an actual question but I really need what you are thinking about it. I have an advisor, not pretty much knowledgeable about Machine Learning/Deep Learning and Statistics. While we were ...
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“Variance is independent of class” assumption

Reading from Logistic Regression slides by Erdem, before deriving the linear boundary equation from the GNB equation it says "What if we assume variance is independent of class i.e. $\sigma^2_{i,0}=\...
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Which Naive Bayes model to use for data converted to dummy?

I have a dataset which has a mix of caterogrical and numerical variables. After filling in the missing data (imputing it with most common occuring value) i then convert my data into dummy e.g the ...
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NaiveBayes: Adding a new predictor which is random noise

In the NaiveBayes method, can adding a random noise vector where each element is sampled from, e.g., a standard normal distribution, help? In what cases this may be a 'clever' approach? I imagine ...
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How can I show Naive Bayes Classifier is using Maximum a Posterior?

I understand how Naive Bayes' Classifier is working and I also understand Maximum a Posterior but what I don't understand is the connection between Naive Bayes' Classifier and Maximum a Posterior. I ...
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Is there a way to know what features a Multinomial Naive Bayes classifier is taking into account to predict categories?

I have created a Multinomial Naive Bayes classifier. The dataset looks like this: ...
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NB or linear SVM for text Classification

I'm trying to compare the time complexity of the NB and the linear SVM when using them for text classification but can't find out a response to what to use considering that the number of features is ...
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Naive Bayes - why MAP?

According to Wikipedia on (Classic) Naive Bayes The naive Bayes classifier combines this model with a decision rule. One common rule is to pick the hypothesis that is most probable; this is known ...
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Implication of marginal independence of features for classification

This question is a follow-up to my earlier question on naive Bayes (NB) classification. The example we're considering is that of spam classification, in which an email is classified as spam ($S \in \{...
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Naive Bayes calculation

I was following along with an example given here in which we are trying to classify emails as spam ($S \in \{0, 1\}$) based on the occurrence of the words "buy" ($B \in \{0, 1\}$) and "cheap" ($C \in \...
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How can a multinomial naive Bayes classifier be used here?

I'm doing a problem from a textbook where the data (found here) are students and the features include sex, family size, health, age, etc. The problem asks to make a naive Bayes classifier from scratch ...
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multi class text classification with multiple dependent variable to one set of predictors in r

I am doing multi class classification in text in r on a dataset containing two columns; feedback and topics. Some feedback has been assigned to more than one topics and some more than two but most of ...
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When to use KNN and when Naive Bayes algorithm

Trying to get clear guidelines on when we should use KNN and when Naive Bayes but not getting anything. The indication I am getting is KNN is a lazy learner and NB is used to spam/ham classification ...
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which better Naive Bayes using Cross Validation or without Cross Validation?

I am the beginner in machine learning and just found out about cross-validation in the algorithm. I build a system that used a Naive Bayes algorithm to classify the activity driving. and now I'm still ...
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Building a Classification model for predicting Customer Churn

I am currently building a Customer Churn Prediction model and the project is in the process of development of models. The client has given data till Sep 2019 and wants to check if the model is able to ...
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How do I split the data using repeated cv?

I am working on a coursework and I have the following task: Formation of training and test sets in R using the methods below: • Repeated CV for Bagging type classifier • Repeated CV for Stacking ...
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What does prediction mean in Bayesian Network and how can I make predictions in Bayesian Network?

I am currently working on a Bayesian Network(1 parent node, 2 children nodes, for example) and want to do predictions on my Bayesian Network. Conditional probabilities are all set and I wonder the way ...
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logic behind balancing? [duplicate]

I am a newbie in stats, and while reading: https://towardsdatascience.com/having-an-imbalanced-dataset-here-is-how-you-can-solve-it-1640568947eb I don't seem to understand why is an imbalanced ...

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