# 나이브 베이즈 분류

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## 노트

### 말뭉치

1. How much do you know about the algorithm called Naive Bayes?
2. To understand the naive Bayes classifier we need to understand the Bayes theorem.
3. Multinomial Naive Bayes is favored to use on data that is multinomial distributed.
4. Bernoulli Naïve Bayes: When data is dispensed according to the multivariate Bernoulli distributions then Bernoulli Naive Bayes is used.
5. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.
6. Now the Naive Bayes comes in here , as it tries to classify based on the vector or the number assigned to the token.
7. Generally, Naive Bayes works best only for small to medium sized data sets.
8. The conventional version of the Naive Bayes is the Gaussian NB, which works best for continuous types of data.
9. Do you want to master the machine learning algorithms like Naive Bayes?
10. What are the Pros and Cons of using Naive Bayes?
11. Naive Bayes uses a similar method to predict the probability of different class based on various attributes.
12. In this article, we looked at one of the supervised machine learning algorithm “Naive Bayes” mainly used for classification.
13. Gaussian Naive Bayes¶ GaussianNB implements the Gaussian Naive Bayes algorithm for classification.
14. CNB is an adaptation of the standard multinomial naive Bayes (MNB) algorithm that is particularly suited for imbalanced data sets.
15. Spam filtering with Naive Bayes – Which Naive Bayes?
16. A Naive Bayes classifier is a probabilistic machine learning model that’s used for classification task.
17. Then finding the conditional probability to use in naive Bayes classifier.
18. Naive Bayes classifiers are built on Bayesian classification methods.
19. Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes.
20. Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution.
21. It is well-known that naive Bayes performs surprisingly well in classification, but its probability estimation is poor.
22. What is the general performance of naive Bayes in ranking?
23. Surprisingly, naive Bayes performs perfectly on them in ranking, even though it does not in classification.
24. Finally, we present and prove a sufficient condition for the optimality of naive Bayes in ranking.
25. For some types of probability models, naive Bayes classifiers can be trained very efficiently in a supervised learning setting.
26. For example, a setting where the Naive Bayes classifier is often used is spam filtering.
27. Naive Bayes leads to a linear decision boundary in many common cases.
28. We're going to be working with an algorithm called Multinomial Naive Bayes.
29. These techniques allow Naive Bayes to perform at the same level as more advanced methods.
30. You now know how Naive Bayes works with a text classifier, but you’re still not quite sure where to start.
31. Hopefully, you now have a better understanding of what Naive Bayes is and how it can be used for text classification.
32. The Naive Bayes classifier is a simple classifier that classifies based on probabilities of events.
33. Naive Bayes that uses a binomial distribution.
34. Naive Bayes that uses a multinomial distribution.
35. Naive Bayes classifiers are a set of probabilistic classifiers that aim to process, analyze, and categorize data.
36. Naive Bayes is essentially a technique for assigning classifiers to a finite set.
37. Naive Bayes classifiers deserve their place in Machine Learning 101 as one of the simplest and fastest algorithms for classification.
38. Class for a Naive Bayes classifier using estimator classes.
39. Naive Bayes classifier gives great results when we use it for textual data analysis.
40. Naive Bayes is a kind of classifier which uses the Bayes Theorem.
41. Naive Bayes classifier assumes that all the features are unrelated to each other.
42. MultiNomial Naive Bayes is preferred to use on data that is multinomially distributed.
1. Then finding the conditional probability to use in naive Bayes classifier.
2. To understand the naive Bayes classifier we need to understand the Bayes theorem.
3. What is the Naïve Bayes Classifier Algorithm and how does it work?
4. After learning about Bayes Theorem, the Naïve Bayes Classifier can be easily understood.
5. In R, Naive Bayes classifier is implemented in packages such as e1071 , klaR and bnlearn .
6. ComplementNaiveBayes builds a Complement Naïve Bayes classifier as described by Rennie et al.
7. This learns a multinomial Naïve Bayes classifier in a combined generative and discriminative fashion.
8. The naïve Bayes classifier combines this model with a decision rule.
9. The assumptions on distributions of features are called the "event model" of the naïve Bayes classifier.
10. L ⊎ U {\displaystyle D=L\uplus U} L and unlabeled samples U , start by training a naïve Bayes classifier on L .
11. Despite the fact that the far-reaching independence assumptions are often inaccurate, the naive Bayes classifier has several properties that make it surprisingly useful in practice.
12. Whether you’re a Machine Learning expert or not, you have the tools to build your own Naive Bayes classifier.
13. A Naive Bayes classifier is a probabilistic machine learning model that’s used for classification task.
14. The Multinomial Naïve Bayes classifier is used when the data is multinomial distributed.
15. Creating Confusion Matrix: Now we will check the accuracy of the Naive Bayes classifier using the Confusion matrix.
16. Visualizing the training set result: Next we will visualize the training set result using Naïve Bayes Classifier.
17. In the above output we can see that the Naïve Bayes classifier has segregated the data points with the fine boundary.
18. Naive Bayes classifier assume that the effect of the value of a predictor ( x ) on a given class ( c ) is independent of the values of other predictors.
19. When most people want to learn about Naive Bayes, they want to learn about the Multinomial Naive Bayes Classifier.
20. Let me explain a Multinomial Naïve Bayes Classifier where we want to filter out the spam messages.
21. Naive Bayes classifier is used in Text Classification, Spam filtering and Sentiment Analysis.
22. With a naive Bayes classifier, each of these three features (shape, size, and color) contributes independently to the probability that this fruit is an orange.
23. Another useful Naïve Bayes classifier is Multinomial Naïve Bayes in which the features are assumed to be drawn from a simple Multinomial distribution.
24. Specifically, we use a naive Bayes classifier model to help the credit-risk manager in explaining why a particular applicant is classified as either bad or good.
25. The above model summarizes a naive Bayes classifier, which assumes that the data X are generated by a mixture of class-conditional (i.e. dependent on the value of the class variable Y) Gaussians.
26. Let’s recall that our objective is to use the naive Bayes classifier methodology for default prediction of a bank’s commercial loans.
27. To get a better idea about our data before running the naive Bayes classifier models, we will perform a test of mean differences between the two risk classes defined above (Table II).
28. The Naive Bayes Classifier brings the power of this theorem to Machine Learning, building a very simple yet powerful classifier.
29. In the Naive Bayes Classifier, we can interpret these Class Probabilities as simply the frequency of each instance of the event divided by the total number of instances.
30. The application of the Naive Bayes Classifier has been shown successful in different scenarios.
31. Then, all that we have to do is initialize the Naive Bayes Classifier and fit the data.
32. Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems.
33. Naive Bayes classifier assumes that the effect of a particular feature in a class is independent of other features.
34. In fact, the graph represented by is the Naive Bayes classifier.