Which is better naive Bayes or decision tree?

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.

Which algorithm is better than decision tree?

Therefore, the random forest can generalize over the data in a better way. This randomized feature selection makes random forest much more accurate than a decision tree.

Why naive Bayes algorithm is best?

Pros: It is easy and fast to predict class of test data set. It also perform 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.

Which is better random forest or naive Bayes?

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.

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.

What is the difference between Naive Bayes and Bayes?

Well, you need to know that the distinction between Bayes theorem and Naive Bayes is that Naive Bayes assumes conditional independence where Bayes theorem does not. This means the relationship between all input features are independent. Maybe not a great assumption, but this is is why the algorithm is called “naive”.

Why is decision tree better than logistic regression?

Decision Trees bisect the space into smaller and smaller regions, whereas Logistic Regression fits a single line to divide the space exactly into two. Of course for higher-dimensional data, these lines would generalize to planes and hyperplanes.

Is decision tree supervised learning?

Introduction Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves.

Why naive Bayes is 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 should I use Naive Bayes?

Why is Naive Bayes good for text classification?

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.