Why naive Bayes works 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.

Can naive Bayes be used for feature selection?

Due to its linear complexity, naive Bayes classification remains an attractive supervised learning method, especially in very large-scale settings. We propose a sparse version of naive Bayes, which can be used for feature selection.

Is naive Bayes good for multiclass classification?

Advantages. It is easy and fast to predict the class of the test data set. It also performs 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.

How can I improve my naive Bayes model?

Better Naive Bayes: 12 Tips To Get The Most From The Naive Bayes Algorithm

1. Missing Data. Naive Bayes can handle missing data.
2. Use Log Probabilities.
3. Use Other Distributions.
4. Use Probabilities For Feature Selection.
5. Segment The Data.
6. Re-compute Probabilities.
7. Use as a Generative Model.
8. Remove Redundant Features.

What does the naive Bayes classifier assume the features are?

In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter.

How do you do the multiclass classification?

Approach –

1. Load dataset from the source.
2. Split the dataset into “training” and “test” data.
3. Train Decision tree, SVM, and KNN classifiers on the training data.
4. Use the above classifiers to predict labels for the test data.
5. Measure accuracy and visualize classification.

What are the different types of naive Bayes classifier?

There are three types of Naive Bayes model under the scikit-learn library:

• Gaussian: It is used in classification and it assumes that features follow a normal distribution.
• Multinomial: It is used for discrete counts.
• Bernoulli: The binomial model is useful if your feature vectors are binary (i.e. zeros and ones).

How do you implement naive Bayes with Sklearn?

First Approach (In case of a single feature)

1. Step 1: Calculate the prior probability for given class labels.
2. Step 2: Find Likelihood probability with each attribute for each class.
3. Step 3: Put these value in Bayes Formula and calculate posterior probability.

How does a naive Bayes classifier work?

The Naive Bayes classifier works on the principle of conditional probability, as given by the Bayes theorem. While calculating the math on probability, we usually denote probability as P. Some of the probabilities in this event would be as follows: The probability of getting two heads = 1/4.

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.

What are the advantages of naive Bayes classifiers?

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. For example, a loan applicant is desirable or not depending on his/her income, previous loan and transaction history, age,…

How to calculate naive Bayes in machine learning?

Now that we know what Naive Bayes is, we can take a closer look at how to calculate the elements of the equation. The calculation of the prior P (yi) is straightforward. It can be estimated by dividing the frequency of observations in the training dataset that have the class label by the total number of examples (rows) in the training dataset.

Which is the fastest and most accurate Bayes classifier?

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.