What are the disadvantages 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.

Why is naive Bayes bad for image classification?

The downside in the Naive Bayes classifier is that it assumes the all the dimensions present in the data set is independent to one another and which we all know that it’s not correct. After training the model, the classifier shows it has understood to label by plotting Mean (μ) of each class.

Which of the following are advantages of the naïve Bayes method of classification?

Advantages of Naive Bayes Classifier It is simple and easy to implement. It doesn’t require as much training data. It handles both continuous and discrete data. It is highly scalable with the number of predictors and data points.

Can I use naive Bayes for classification?

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.

What are the pros and cons of naive Bayes classifier?

Pros and Cons of Naive Bayes Algorithm

• The assumption that all features are independent makes naive bayes algorithm very fast compared to complicated algorithms. In some cases, speed is preferred over higher accuracy.
• It works well with high-dimensional data such as text classification, email spam detection.

Which of the following are the disadvantages of using Knn?

Some Disadvantages of KNN

• Accuracy depends on the quality of the data.
• With large data, the prediction stage might be slow.
• Sensitive to the scale of the data and irrelevant features.
• Require high memory – need to store all of the training data.
• Given that it stores all of the training, it can be computationally expensive.

Is naive Bayes a bad classifier?

In scikit-learn documentation page for Naive Bayes, it states that: On the flip side, although naive Bayes is known as a decent classifier, it is known to be a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously.

Why is naive Bayes not accurate?

Naive Bayes will not be reliable if there are significant differences in the attribute distributions compared to the training dataset. An important example of this is the case where a categorical attribute has a value that was not observed in training.

What are the limitations of using naive Bayes algorithm to detect spam?

Disadvantages – A subtle issue with Naive-Bayes Classifier is that if you have no occurrences of a class label and a certain attribute value together then the frequency-based probability estimation will be zero. A big data set is required for making reliable predictions of the probability of each class.

What are the advantages and disadvantages of naive Bayes classifier?

What are the Advantages and Disadvantages of Naïve Bayes Classifier? 1. When assumption of independent predictors holds true, a Naive Bayes classifier performs better as compared to other models. 2. Naive Bayes requires a small amount of training data to estimate the test data. So, the training period is less.

What is nanaive Bayes and how does it work?

Naive Bayes uses the Bayes’ Theorem and assumes that all predictors are independent. In other words, this classifier assumes that the presence of one particular feature in a class doesn’t affect the presence of another one. Here’s an example: you’d consider fruit to be orange if it is round, orange, and is of around 3.5 inches in diameter.

What is ‘zero frequency’ in 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. This phenomenon is called ‘Zero Frequency,’ and you’ll have to use a smoothing technique to solve this problem.

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.