Which algorithm is better than Naive Bayes?

Logistic Regression vs Naive Bayes : LR performs better than naive bayes upon colinearity, as naive bayes expects all features to be independent.

Why Naive Bayes algorithm is best?

Pros: It is easy and fast to predict class of test data set. It also perform well in multi class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.

Is Naive Bayes the best classifier?

Results show that Naïve Bayes is the best classifiers against several common classifiers (such as decision tree, neural network, and support vector machines) in term of accuracy and computational efficiency.

In which case Naive Bayes is useful?

The Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. Naïve Bayes performs well in cases of categorical input variables compared to numerical variables. It is useful for making predictions and forecasting data based on historical results.

Which is faster decision tree or Naive Bayes?

The classifiers we use are Naïve Bayes, Decision Tree, and k-Nearest Neighbor. Our experiments hows that Decision Tree has the fastest classification time followed by Naïve Bayes and k-Nearest Neighbor.

Is naive Bayes accurate?

Naive Bayes classifier is the fast, accurate and reliable algorithm. Naive Bayes classifiers have high accuracy and speed on large datasets. Naive Bayes classifier assumes that the effect of a particular feature in a class is independent of other features.

What are advantages and disadvantages of naive Bayes algorithm?

Naive Bayes is suitable for solving multi-class prediction problems. If its assumption of the independence of features holds true, it can perform better than other models and requires much less training data. Naive Bayes is better suited for categorical input variables than numerical variables.

What is the limitation of naive Bayes algorithm?

Disadvantages of Naive Bayes Main imitation of Naive Bayes is the assumption of independent predictors. Naive Bayes implicitly assumes that all the attributes are mutually independent. In real life, it is almost impossible that we get a set of predictors which are completely independent. 2.

Why is naive Bayes good for text classification?

As the Naive Bayes algorithm has the assumption of the “Naive” features it performs much better than other algorithms like Logistic Regression, Tree based algorithms etc. The Naive Bayes classifier is much faster with its probability calculations.

What are the advantages and disadvantages of naive Bayes algorithm?

To get a better picture of Naive Bayes explained, we should now discuss its advantages and disadvantages: This algorithm works quickly and can save a lot of time. Naive Bayes is suitable for solving multi-class prediction problems.

What are nanaive Bayes classifiers?

Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

What is categorical naive Bayes example?

Categorical Naive Bayes Categorical Naive Bayes is suitable for the categorical values — if the example has the set of features or not. In our case, it means, that the vocabulary is treated as the set of features, and the occurrence of a word in the message is treated as the matching with the feature.

What is p(x|c) in naive Bayes?

P (c) is the prior probability of the class, P (x) is the prior probability of the predictor, and P (x|c) is the probability of the predictor for the particular class (c). Apart from considering the independence of every feature, Naive Bayes also assumes that they contribute equally.