# 나이브 베이즈 분류

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## 노트

### 말뭉치

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

## 메타데이터

### Spacy 패턴 목록

• [{'LOWER': 'naive'}, {'LOWER': 'bayes'}, {'LEMMA': 'classifier'}]
• [{'LOWER': 'naive'}, {'LEMMA': 'Bayes'}]