What is the advantage of naive Bayesian 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.
What is the use of naive Bayesian classifier?
This algorithm works very fast and can easily predict the class of a test dataset. You can use it to solve multi-class prediction problems as it’s quite useful with them. Naive Bayes classifier performs better than other models with less training data if the assumption of independence of features holds.
What is the use of naive Bayes algorithm in data mining?
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.
What is naive Bayes classifier in data mining?
Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.
What are the advantages and disadvantages of using Naïve 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.
Why is Naïve Bayes better than Knn?
Naive Bayes is a linear classifier while K-NN is not; It tends to be faster when applied to big data. In comparison, k-nn is usually slower for large amounts of data, because of the calculations required for each new step in the process. In general, Naive Bayes is highly accurate when applied to big data.
What are the advantages and disadvantages of naive Bayes classifier?
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.
Why is naive Bayes 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 are the advantages of SVM?
SVM works relatively well when there is a clear margin of separation between classes. SVM is more effective in high dimensional spaces. SVM is effective in cases where the number of dimensions is greater than the number of samples. SVM is relatively memory efficient.
What are the advantages and disadvantages of decision trees?
Advantages and Disadvantages of Decision Trees in Machine Learning. Decision Tree is used to solve both classification and regression problems. But the main drawback of Decision Tree is that it generally leads to overfitting of the data.
Why logistic regression is better than Naive Bayes?
If the data set follows the bias then Naive Bayes will be a better classifier. Both Naive Bayes and Logistic regression are linear classifiers, Logistic Regression makes a prediction for the probability using a direct functional form where as Naive Bayes figures out how the data was generated given the results.