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A simple explanation of Naive Bayes Classification
Naive Bayes comes under supervising machine learning which used to make classifications of data sets. It is used to predict things based on its prior knowledge and independence assumptions. They call it naive because it’s assumptions (it assumes that all of the features in the dataset are equally important and independent) are really ...
What is "naive" in a naive Bayes classifier? - Stack Overflow
In simple terms, a naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 4" in diameter.
scikit learn - How to perform grid search on multinomial naive Bayes ...
I'm wondering how do we do grid search with multinomial naive bayes classifiers? Here is my multinomial classifiers: import numpy as np from collections import Counter from sklearn.grid_search im...
What is the difference between a Bayesian network and a naive Bayes ...
A good paper to read on this is "Bayesian Network Classifiers, Machine Learning, 29, 131–163 (1997)". Of particular interest is section 3. Though Naive Bayes is a constrained form of a more general Bayesian network, this paper also talks about why Naive Bayes can and does outperform a general Bayesian network in classification tasks.
ImportError: No module named naive_bayes - Stack Overflow
I just installed sklearn, my program runs no problem when I import it into the code. However, whenever I try to access the naive_bayes module, I get this error: ImportError: No module named naive_bayes Here's how I'm importing it: from sklearn.naive_bayes import GaussianNB Not sure where I'm going wrong, any help is much appreciated!
Example of how the log-sum-exp trick works in Naive Bayes
More specifically, in order to prevent underflows: If we only care about knowing which class $(\hat{y})$ the input $(\mathbf{x}=x_1, \dots, x_n)$ most likely belongs to with the maximum a posteriori (MAP) decision rule, we don't have to apply the log-sum-exp trick, since we don't have to compute the denominator in that case.
Ways to improve the accuracy of a Naive Bayes Classifier?
In my experience, properly trained Naive Bayes classifiers are usually astonishingly accurate (and very fast to train--noticeably faster than any classifier-builder i have everused). so when you want to improve classifier prediction, you can look in several places: tune your classifier (adjusting the classifier's tunable paramaters);
python - Mixing categorial and continuous data in Naive Bayes ...
Naive Bayes based on applying Bayes’ theorem with the “naive” assumption of independence between every pair of features - meaning you calculate the Bayes probability dependent on a specific feature without holding the others - which means that the algorithm multiply each probability from one feature with the probability from the second ...
history - Origin of the Naïve Bayes classifier? - Cross Validated
A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions. Bayes' theorem was named after the Reverend Thomas Bayes (1702–61), who studied how to compute a distribution for the probability parameter of a binomial distribution. After Bayes' death, his friend ...
python - Naive Bayes: Imbalanced Test Dataset - Stack Overflow
You can think of Naive Bayes as learning a probability distribution, in this case of words belonging to topics. So the balance of the training data matters. If you use decision trees, say a random forest model, you learn rules for making the assignment (yes there are probability distributions involved and I apologise for the hand waving ...
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