Sage-Tips

Why naive Bayes works well with many number of features?

Because of the class independence assumption, naive Bayes classifiers can quickly learn to use high dimensional features with limited training data compared to more sophisticated methods. This can be useful in situations where the dataset is small compared to the number of features, such as images or texts.

How do I overcome Overfitting in naive Bayes?

The Naive Bayes classifier employs a very simple (linear) hypothesis function. On the other hand, it exhibits low variance or failure to generalize to unseen data based on its training set, because it’s hypothesis class’ simplicity prevents it from overfitting to its training data.

Which distribution should be used for naive Bayes when the majority of features are continuous?

A Gaussian distribution is usually chosen to represent the class-conditional probability for continuous attributes.

What is Naive Bayes explain with the help of example?

Naive Bayes is a probabilistic algorithm that’s typically used for classification problems. Naive Bayes is simple, intuitive, and yet performs surprisingly well in many cases. For example, spam filters Email app uses are built on Naive Bayes.

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).

What is the Naive Bayes algorithm used for?

Naive Bayes uses a similar method to predict the probability of different class based on various attributes. This algorithm is mostly used in text classification and with problems having multiple classes.

How do you get a feature important in naive Bayes?

The naive bayes classifers don’t offer an intrinsic method to evaluate feature importances. Naïve Bayes methods work by determining the conditional and unconditional probabilities associated with the features and predict the class with the highest probability.

What is naive Bayes in machine learning?

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.

What is nanaive Bayes?

Naive Bayes is a type of supervised learning algorithm which comes under the Bayesian Classification . It uses probability for doing its predictive analysis .

How do you use naive Bayes in Excel?

Select a cell on the Data_Partition worksheet, then on the XLMiner ribbon, from the Data Mining tab, select Classify – Naïve Bayes to open the Naïve Bayes – Step 1 of 3 dialog. From the Selected Variables list, select Var2, Var3, Var4, Var5, and Var6, and at Output Variable, select TestRest/Var1.

What is the best way to train a naive Bayes classifier?

Assuming you already have a workflow for building Naive Bayes classifiers, you might want to consider Boosting. Generally, these methods would train several weaker classifiers in a way which results with a stronger classifier. Boosting Naive Bayes classifiers has been shown to work nicely, e.g. see here.