What is the difference between naive Bayes and Bayesian classifier
Structure: Naive Bayes is a simple probabilistic classifier, while Bayesian networks are a type of probabilistic graphical model that represents the relationship between variables in the form of a directed acyclic graph (DAG).
Why is Naive Bayes classifier called as Naive Bayes
Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data; however, the technique is very effective on a large range of complex problems.
Is naive Bayes and naive Bayesian same
In the statistics literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method.
What are the different types of Naive Bayes classifier
TypesGaussian Naive Bayes classifier: used when features are not discreet.Multinomial Naive Bayes Classifier: used when features follow a multinomial distribution.Bernoulli Naive Bayes classifier: used when features are of the boolean type.
What is the relationship between naïve Bayesian classifier and Bayesian network
A Naive Bayes classifier is a simple model that describes particular class of Bayesian network – where all of the features are class-conditionally independent. Because of this, there are certain problems that Naive Bayes cannot solve (example below).
Why Bayesian classifier is a naive classifier
A. Naive Bayes classifier assumes features are independent of each other. Since that is rarely possible in real-life data, the classifier is called naive.
Why not use Naive Bayes classifier
Disadvantages of Using Naive Bayes Classifier
In most situations, the feature show some form of dependency. Zero probability problem : When we encounter words in the test data for a particular class that are not present in the training data, we might end up with zero class probabilities.
What is the difference between Naive Bayes and decision tree classifier
Decision tree vs naive Bayes :
Decision tree is a discriminative model, whereas Naive bayes is a generative model. Decision trees are more flexible and easy. Decision tree pruning may neglect some key values in training data, which can lead the accuracy for a toss.
What is the use of naive Bayesian classifier
Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions. It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.
What is the difference between Bayesian and neural network
Bayesian Neural Networks (BNN) are different from Artificial Neural Networks (NN). The main difference—BNNs can respond “I'm not sure”. Which is interesting, but why would you want a neural network to tell you that it doesn't know the answer to your question
What is the limitation of Naive Bayes Classifier
Disadvantages of Naive Bayes
If your test data set has a categorical variable of a category that wasn't present in the training data set, the Naive Bayes model will assign it zero probability and won't be able to make any predictions in this regard.
What is the difference between naive Basian and KNN classifiers
Naive Bayes is a linear classifier while K-NN is not; It tends to be faster when applied to big data. In comparison, k-nn is usually slower for large amounts of data, because of the calculations required for each new step in the process. If speed is important, choose Naive Bayes over K-NN.
What is the difference between a classification tree and a decision tree
Whereas, classification is used when we are trying to predict the class that a set of features should fall into. A decision tree can be used for either regression or classification. It works by splitting the data up in a tree-like pattern into smaller and smaller subsets.
What is the difference between decision tree and Naive Bayes
Decision trees work better with lots of data compared to Naive Bayes. Naive Bayes is used a lot in robotics and computer vision, and does quite well with those tasks. Decision trees perform very poorly in those situations.
Is Naive Bayes a linear classifier
Naive Bayes is a linear classifier
The boundary of the ellipsoids indicate regions of equal probabilities P(x|y). The red decision line indicates the decision boundary where P(y=1|x)=P(y=2|x).
Is Naive Bayes a neural network
The naive Bayesian classifier can be implemented in a directional two-layered or multidirectional single-layered Bayesian neural network (BNN). Similarly, the semi-naive Bayesian classifier can be implemented in a directional or multidirectional multi-layered Bayesian neural network.
What is the difference between Bayesian and machine learning
The difference between results from machine learning models and a Bayes Net is that the latter can tell you how likely it is to see a specific pattern of data if a particular hypothesis is true. A Bayes Net is a probabilistic generative model. A generative means you already know something and start with a hypothesis).
Why not use naïve Bayes classifier
Disadvantages of Using Naive Bayes Classifier
In most situations, the feature show some form of dependency. Zero probability problem : When we encounter words in the test data for a particular class that are not present in the training data, we might end up with zero class probabilities.
Why Naive Bayes is not good for classification
On the other side naive Bayes is also known as a bad estimator, so the probability outputs are not to be taken too seriously. Another limitation of Naive Bayes is the assumption of independent predictors. In real life, it is almost impossible that we get a set of predictors which are completely independent.
What is the difference between KNN and NB classifier
Naive Bayes is a linear classifier while K-NN is not; It tends to be faster when applied to big data. In comparison, k-nn is usually slower for large amounts of data, because of the calculations required for each new step in the process.
How is naive Bayes classifier different from K-means
K-Means clustering is used to cluster all data into the corresponding group based on data behavior, i.e. malicious and non-malicious, while the Naïve Bayes classifier is used to classify clustered data into correct categories, i.e. R2L, U2R, Probe, DoS and Normal.
What are the 2 main types of decision trees
Decision trees in machine learning can either be classification trees or regression trees. Together, both types of algorithms fall into a category of “classification and regression trees” and are sometimes referred to as CART.
What is the difference between decision tree and decision list
The key difference is that decision trees may be viewed as unordered Rule Sets, where each leaf of the tree corresponds to a single rule with a condition part consisting of the conjunction of all edge labels on the path from the root to this leaf.
What is the difference between decision tree and Bayesian classification
Bayes does quite well when the training data doesn't contain all possibilities so it can be very good with low amounts of data. Decision trees work better with lots of data compared to Bayes.
What is better than Naïve Bayes classifier
If having conditional independence will highly negative affect classification, you'll want to choose K-NN over Naive Bayes. Naive Bayes can suffer from the zero probability problem; when a particular attribute's conditional probability equals zero, Naive Bayes will completely fail to produce a valid prediction.
