Which is more accurate Naive Bayes or decision tree?

Which is more accurate Naive Bayes or decision tree?

The final results showed that Naive Bayes has higher accuracy than the other two classifiers. The accuracy of the Naive Bayes was 95.2% which higher than Decision Tree’s accuracy rate which was 94.85%. In papers [20, 21, 22] Naive Bayes is also considered better than other classification techniques.

What is Bayes net used for?

Bayesian networks are a type of Probabilistic Graphical Model that can be used to build models from data and/or expert opinion. They can be used for a wide range of tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction and decision making under uncertainty.

Why is Naive Bayes not good?

The Zero-Frequency Problem

One of the disadvantages of Naïve-Bayes is that if you have no occurrences of a class label and a certain attribute value together then the frequency-based probability estimate will be zero. And this will get a zero when all the probabilities are multiplied.

What are the pros and cons of using Naive Bayes?

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.

Why does decision tree perform better than naive Bayes?

Decision tree vs naive Bayes :
Decision tree is a discriminative model, whereas Naive bayes is a generative model. Decision trees are more flexible and easy. Decision tree pruning may neglect some key values in training data, which can lead the accuracy for a toss.

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.

How do you make a Bayes net?

There are three main steps to create a BN :

1. First, identify which are the main variable in the problem to solve.
2. Second, define structure of the network, that is, the causal relationships between all the variables (nodes).
3. Third, define the probability rules governing the relationships between the variables.

What are the advantages of using a naive Bayes for 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.

Is Naive Bayes good for large datasets?

Implementing Naive Bayes Algorithm from Scratch — Python.
It is simple but very powerful algorithm which works well with large datasets and sparse matrices, like pre-processed text data which creates thousands of vectors depending on the number of words in a dictionary. …

Why Naive Bayes is best for classification?

Advantages of Naive Bayes
Naive Bayes classifier performs better than other models with less training data if the assumption of independence of features holds. If you have categorical input variables, the Naive Bayes algorithm performs exceptionally well in comparison to numerical variables.

What is a major weakness of the Naive Bayes classifier?

Naive Bayes assumes that all predictors (or features) are independent, rarely happening in real life. This limits the applicability of this algorithm in real-world use cases.

What are the limitations of Naive Bayes?

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 naive Bayes is fast?

Learn a Naive Bayes Model From Data
Training is fast because only the probability of each class and the probability of each class given different input (x) values need to be calculated. No coefficients need to be fitted by optimization procedures.

Which is better SVM or decision tree?

The lowest overall accuracy is Decision Tree (DT) with 68.7846%. This means that image classification using Support Vector Machine (SVM) method is better than Decision Tree (DT) in this study. The result shows that the SVM algorithm gives better classification image than DT algorithm.

Which of the following is main drawback of naive Bayes classifier?

Why Naive Bayes is fast?

What is the difference between Markov networks and Bayesian networks?

A Markov network or MRF is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed and acyclic, whereas Markov networks are undirected and may be cyclic.

How Bayesian belief nets are designed?

Bayesian Belief Network is a graphical representation of different probabilistic relationships among random variables in a particular set. It is a classifier with no dependency on attributes i.e it is condition independent.

Why is Naive Bayes better than Knn?

Naive bayes is much faster than KNN due to KNN’s real-time execution. Naive bayes is parametric whereas KNN is non-parametric.

Why Naive Bayes works well with large data?

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.

Why Naive Bayes work very well with many number of features?

Is Naive Bayes good for sentiment analysis?

Naive Bayes is the simplest and fastest classification algorithm for a large chunk of data. In various applications such as spam filtering, text classification, sentiment analysis, and recommendation systems, Naive Bayes classifier is used successfully.

In what real world applications is Naive Bayes classifier used?

Applications of Naïve Bayes Classifier:
It is used for Credit Scoring. It is used in medical data classification. It can be used in real-time predictions because Naïve Bayes Classifier is an eager learner. It is used in Text classification such as Spam filtering and Sentiment analysis.

What is the main assumption in the Naive Bayes classifier?

What is Naive Bayes algorithm? It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. 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.

How do you increase the accuracy of Naive Bayes?

3. Ways to Improve Naive Bayes Classification Performance

1. 3.1. Remove Correlated Features.
2. 3.2. Use Log Probabilities.
3. 3.3. Eliminate the Zero Observations Problem.
4. 3.4. Handle Continuous Variables.
5. 3.5. Handle Text Data.
6. 3.6. Re-Train the Model.
7. 3.7. Parallelize Probability Calculations.
8. 3.8. Usage with Small Datasets.