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)...
  • 326
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 ...
  • 699
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 ...
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 ...
  • 121
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 ...
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 ...
  • 889
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 ...
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 ...
  • 1,683
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 ...
  • 34.9k
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 ...
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 ...
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.
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 ...
  • 11

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