Table of Contents
Is Naive Bayes better than random forest?
According to the findings, the Random Forest classifier performed better than the Naïve Bayes method by reaching a 97.82\% of accuracy. Furthermore, classification accuracy can be improved with the appropriate selection of the feature selection technique.
Why is Naive Bayes preferred?
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.
For what problem Naive Bayes classifier works best?
When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data. It perform well in case of categorical input variables compared to numerical variable(s).
What is advantage of Naive Bayes over decision tree?
Naive 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 Naive Bayes. Naive Bayes is used a lot in robotics and computer vision, and does quite well with those tasks.
Why is naive Bayes called naive?
Naive Bayes is called naive because it assumes that each input variable is independent. The thought behind naive Bayes classification is to try to classify the data by maximizing P(O | Ci)P(Ci) using Bayes theorem of posterior probability (where O is the Object or tuple in a dataset and “i” is an index of the class).
When can we use naive Bayes?
Naive Bayes is the most straightforward and fast classification algorithm, which is suitable for a large chunk of data. Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems.
Why is naive Bayes better than logistic regression for text classification?
Naive Bayes also assumes that the features are conditionally independent. Real data sets are never perfectly independent but they can be close. In short Naive Bayes has a higher bias but lower variance compared to logistic regression. If the data set follows the bias then Naive Bayes will be a better classifier.
How does Gaussian naive Bayes work?
Gaussian Naive Bayes supports continuous valued features and models each as conforming to a Gaussian (normal) distribution. An approach to create a simple model is to assume that the data is described by a Gaussian distribution with no co-variance (independent dimensions) between dimensions.
What are the advantages and disadvantages of using Naive Bayes?
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.
Why are naive Bayes classifiers so popular?
Naive Bayes classifiers are a popular choice for classification problems. There are many reasons for this, including: However, they are ‘naive’ – i.e. they assume the features are independent – this contrasts with other classifiers such as Maximum Entropy classifiers (which are slow to compute).
What is the difference between naive Bayes and supervised learning?
As other supervised learning algorithms, naive bayes uses features to make a prediction on a target variable. The key difference is that naive bayes assumes that features are independent of each other and there is no correlation between features. However, this is not the case in real life.
What is Gaussian naive Bayes distribution?
A Gaussian distribution is also called Normal distribution. When plotted, it gives a bell shaped curve which is symmetric about the mean of the feature values as shown below: Now, we look at an implementation of Gaussian Naive Bayes classifier using scikit-learn.
Is Bayes only good when features are independent?
This paper seems to prove (I can’t follow the math) that bayes is good not only when features are independent, but also when dependencies of features from each other are similar between features: In this paper, we propose a novel explanation on the superb classification performance of naive Bayes.