4 votes
Accepted

Why is the denominator ignored in the Bayes' rule?

The answer from @kiner_shah in the comments has solved it: They have eliminated it because in comparing probability for best outcome, it would just introduce additional division and the divisor p(x)...
gcorso's user avatar
  • 356
3 votes

What is the relationship between MLE and naive Bayes?

And that's all, we can infer P(x|y=c) and P(c) from the data. I don't see where the MLE shows its role. Maximum likelihood estimate is used for this very purpose, i.e. to estimate the conditional ...
naive's user avatar
  • 699
3 votes

Doesn't every single machine learning classifier use conditional probability/Bayes in its underlying assumptions?

Conditional probability and Bayes rule are related but they are not the same thing, you can predict conditional probabilities without using Bayes rule. So no, not all machine learning classifiers use ...
Dr. Snoopy's user avatar
  • 1,320
2 votes

Doesn't every single machine learning classifier use conditional probability/Bayes in its underlying assumptions?

Probability is one way to solve classification problems. Still, there are other ways like clustering and K nearest neighbor approach where we tend to analyze the position of the current data point and ...
oseekero's user avatar
2 votes
Accepted

How to predict the rating of a text review and improve it?

Why is it better to treat the rating prediction of a text review as a regression problem rather than a classification one? Is it because the ratings (1,2,3,4,5) are ordinal variables? Well, the main ...
Green Falcon's user avatar
2 votes

Why is my calculation of the probability of an object being in a certain class incorrect?

Your probability hasn't been normalized! In this case, you are computing the probability of being good, given that the other features have a fixed value. To obtain the correct probability, you need ...
John Doucette's user avatar
2 votes

Why would LDA have performed much better than SVM and Naive Bayes in diagnosing ADHD?

It would be hard to tell if you don't provide what kind of data/problem you are working on, but LDA works well when data that are grouped in gaussian blobs surrounding centroids while vanilla SVM ...
Kevin He's user avatar
  • 121
1 vote
Accepted

Is logistic regression more free from the conditional independence assumption than naive Bayes?

I refer you to Prof. Tom Mitchell's (the lecturer in the video) draft chapter as the best explanation I could find. I will try to explain it in layman's terms here. Given a boolean problem, our ...
Abdul Rahman Dabbour's user avatar
1 vote

What are the main differences between a perceptron and a naive Bayes classifier?

A perceptron is a linear threshold function. That means it has a weight vector $w$, and it outputs $w \cdot x > t$, where $x$ is the input vector and $t$ the threshold. Naïve Bayes makes the ...
chessprogrammer's user avatar
1 vote

What are the main differences between a perceptron and a naive Bayes classifier?

Naive Bayes is a generative algorithm while Perceptron is a discriminative algorithm. That is the main difference.
Abhishek Verma's user avatar
1 vote

What is the most effective way to build a classifier?

Your approach would definitely work. I would recommend training a variety of classifiers and comparing their performance using multiclass roc analysis. Also, think about other useful features in ...
chad39's user avatar
  • 11
1 vote

Is it ok to have an accuracy of 65% and a sensitivity of 90% with Naive Bayes for sentiment analysis?

I could get perfect sensitivity for positive sentiment if I always predict positive sentiment, but my accuracy could be 50%ish depending on the distribution of positive sentiment in the data. The ...
Cameron Chandler's user avatar
1 vote
Accepted

Understanding how to calculate $P(x|c_k)$ for the Bernoulli naïve Bayes classifier

Bernoulli naïve Bayes $P(x \mid c_k) = \prod^{n}_{i=1} p^{x_i}_{ki} (1-p_{ki})^{(1-x_i)}$ Let's examine the example of document classification. Let K different text classes and n different terms that ...
ddaedalus's user avatar
  • 919
1 vote

What is the meaning of test data set in naive bayes classifier or decision trees?

In machine learning, we can use all the datasets as training data in a model. But if there are too many data sets, or too much data, and we do not split them up, our model may be not produce ...
Muhammad Mukhtar's user avatar
1 vote

What is the meaning of test data set in naive bayes classifier or decision trees?

Your assumption about the test data is not correct completely. Maybe you use the test data to tune your learning algorithm to work better on the test data, but it's not the whole thing. Sometimes you ...
OmG's user avatar
  • 1,816
1 vote

Why do Bayesian algorithms work well with small datasets?

The main reason should be that Bayesian algorithms naturally incorporate a form of regularisation (the prior), so they should be less prone to over-fitting the small dataset. Of course, the choice of ...
nbro's user avatar
  • 40.1k
1 vote

What to do when PDFs are not Gaussian/Normal in Naive Bayes Classifier

The relationship between the axes of graph (1) and your variables $x$ and $y$ is not clear, so this generalized answer may be helpful or useless. From graph (1) it appears that the correlation ...
Douglas Daseeco's user avatar

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