# 나이브 베이즈 분류

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## 관련된 항목들

## 노트

### 위키데이터

- ID : Q812530

### 말뭉치

- How much do you know about the algorithm called Naive Bayes?
^{[1]} - To understand the naive Bayes classifier we need to understand the Bayes theorem.
^{[2]} - Multinomial Naive Bayes is favored to use on data that is multinomial distributed.
^{[2]} - Bernoulli Naïve Bayes: When data is dispensed according to the multivariate Bernoulli distributions then Bernoulli Naive Bayes is used.
^{[2]} - Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.
^{[3]} - Now the Naive Bayes comes in here , as it tries to classify based on the vector or the number assigned to the token.
^{[3]} - Generally, Naive Bayes works best only for small to medium sized data sets.
^{[3]} - The conventional version of the Naive Bayes is the Gaussian NB, which works best for continuous types of data.
^{[3]} - Do you want to master the machine learning algorithms like Naive Bayes?
^{[4]} - What are the Pros and Cons of using Naive Bayes?
^{[4]} - Naive Bayes uses a similar method to predict the probability of different class based on various attributes.
^{[4]} - In this article, we looked at one of the supervised machine learning algorithm “Naive Bayes” mainly used for classification.
^{[4]} - Gaussian Naive Bayes¶ GaussianNB implements the Gaussian Naive Bayes algorithm for classification.
^{[5]} - CNB is an adaptation of the standard multinomial naive Bayes (MNB) algorithm that is particularly suited for imbalanced data sets.
^{[5]} - Spam filtering with Naive Bayes – Which Naive Bayes?
^{[5]} - A Naive Bayes classifier is a probabilistic machine learning model that’s used for classification task.
^{[6]} - Then finding the conditional probability to use in naive Bayes classifier.
^{[7]} - Naive Bayes classifiers are built on Bayesian classification methods.
^{[8]} - Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes.
^{[8]} - Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution.
^{[8]} - It is well-known that naive Bayes performs surprisingly well in classification, but its probability estimation is poor.
^{[9]} - What is the general performance of naive Bayes in ranking?
^{[9]} - Surprisingly, naive Bayes performs perfectly on them in ranking, even though it does not in classification.
^{[9]} - Finally, we present and prove a sufficient condition for the optimality of naive Bayes in ranking.
^{[9]} - For some types of probability models, naive Bayes classifiers can be trained very efficiently in a supervised learning setting.
^{[10]} - For example, a setting where the Naive Bayes classifier is often used is spam filtering.
^{[11]} - Naive Bayes leads to a linear decision boundary in many common cases.
^{[11]} - We're going to be working with an algorithm called Multinomial Naive Bayes.
^{[12]} - These techniques allow Naive Bayes to perform at the same level as more advanced methods.
^{[12]} - You now know how Naive Bayes works with a text classifier, but you’re still not quite sure where to start.
^{[12]} - Hopefully, you now have a better understanding of what Naive Bayes is and how it can be used for text classification.
^{[12]} - The Naive Bayes classifier is a simple classifier that classifies based on probabilities of events.
^{[13]} - Naive Bayes that uses a binomial distribution.
^{[14]} - Naive Bayes that uses a multinomial distribution.
^{[14]} - Naive Bayes classifiers are a set of probabilistic classifiers that aim to process, analyze, and categorize data.
^{[15]} - Naive Bayes is essentially a technique for assigning classifiers to a finite set.
^{[15]} - Naive Bayes classifiers deserve their place in Machine Learning 101 as one of the simplest and fastest algorithms for classification.
^{[16]} - Class for a Naive Bayes classifier using estimator classes.
^{[17]} - Naive Bayes classifier gives great results when we use it for textual data analysis.
^{[18]} - Naive Bayes is a kind of classifier which uses the Bayes Theorem.
^{[18]} - Naive Bayes classifier assumes that all the features are unrelated to each other.
^{[18]} - MultiNomial Naive Bayes is preferred to use on data that is multinomially distributed.
^{[18]}

- Then finding the conditional probability to use in naive Bayes classifier.
^{[19]} - To understand the naive Bayes classifier we need to understand the Bayes theorem.
^{[20]} - What is the Naïve Bayes Classifier Algorithm and how does it work?
^{[21]} - After learning about Bayes Theorem, the Naïve Bayes Classifier can be easily understood.
^{[21]} - In R, Naive Bayes classifier is implemented in packages such as e1071 , klaR and bnlearn .
^{[22]} - ComplementNaiveBayes builds a Complement Naïve Bayes classifier as described by Rennie et al.
^{[23]} - This learns a multinomial Naïve Bayes classifier in a combined generative and discriminative fashion.
^{[23]} - The naïve Bayes classifier combines this model with a decision rule.
^{[24]} - The assumptions on distributions of features are called the "event model" of the naïve Bayes classifier.
^{[24]} - L ⊎ U {\displaystyle D=L\uplus U} L and unlabeled samples U , start by training a naïve Bayes classifier on L .
^{[24]} - 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]} - Whether you’re a Machine Learning expert or not, you have the tools to build your own Naive Bayes classifier.
^{[25]} - A Naive Bayes classifier is a probabilistic machine learning model that’s used for classification task.
^{[26]} - The Multinomial Naïve Bayes classifier is used when the data is multinomial distributed.
^{[27]} - Creating Confusion Matrix: Now we will check the accuracy of the Naive Bayes classifier using the Confusion matrix.
^{[27]} - Visualizing the training set result: Next we will visualize the training set result using Naïve Bayes Classifier.
^{[27]} - In the above output we can see that the Naïve Bayes classifier has segregated the data points with the fine boundary.
^{[27]} - 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]} - When most people want to learn about Naive Bayes, they want to learn about the Multinomial Naive Bayes Classifier.
^{[29]} - Let me explain a Multinomial Naïve Bayes Classifier where we want to filter out the spam messages.
^{[29]} - Naive Bayes classifier is used in Text Classification, Spam filtering and Sentiment Analysis.
^{[29]} - 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]} - Another useful Naïve Bayes classifier is Multinomial Naïve Bayes in which the features are assumed to be drawn from a simple Multinomial distribution.
^{[31]} - 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]} - 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]} - Let’s recall that our objective is to use the naive Bayes classifier methodology for default prediction of a bank’s commercial loans.
^{[32]} - 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]} - The Naive Bayes Classifier brings the power of this theorem to Machine Learning, building a very simple yet powerful classifier.
^{[33]} - 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]} - The application of the Naive Bayes Classifier has been shown successful in different scenarios.
^{[33]} - Then, all that we have to do is initialize the Naive Bayes Classifier and fit the data.
^{[33]} - Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems.
^{[34]} - 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.
^{[35]}

### 소스

- ↑ Understanding Naive Bayes Classifier
- ↑
^{2.0}^{2.1}^{2.2}What Is Naive Bayes Algorithm In Machine Learning? - ↑
^{3.0}^{3.1}^{3.2}^{3.3}Naïve Bayes for Machine Learning – From Zero to Hero - ↑
^{4.0}^{4.1}^{4.2}^{4.3}Naive Bayes Classifier Examples - ↑
^{5.0}^{5.1}^{5.2}1.9. Naive Bayes — scikit-learn 0.23.2 documentation - ↑ Naive Bayes Classifier
- ↑ Naïve Bayes Algorithm: Everything you need to know
- ↑
^{8.0}^{8.1}^{8.2}In Depth: Naive Bayes Classification - ↑
^{9.0}^{9.1}^{9.2}^{9.3}Naive Bayesian Classifiers for Ranking - ↑ Naive Bayes classifier
- ↑
^{11.0}^{11.1}Lecture 5: Bayes Classifier and Naive Bayes - ↑
^{12.0}^{12.1}^{12.2}^{12.3}A practical explanation of a Naive Bayes classifier - ↑ Naive Bayes Classifier for Text Classification
- ↑
^{14.0}^{14.1}How to Develop a Naive Bayes Classifier from Scratch in Python - ↑
^{15.0}^{15.1}Naive Bayes Classifiers - ↑ Naive Bayes classifiers in TensorFlow
- ↑ NaiveBayes
- ↑
^{18.0}^{18.1}^{18.2}^{18.3}How the Naive Bayes Classifier works in Machine Learning - ↑ Naïve Bayes Algorithm: Everything you need to know
- ↑ What Is Naive Bayes Algorithm In Machine Learning?
- ↑
^{21.0}^{21.1}What is the Naïve Bayes Classifier Algorithm and how does it work? - ↑ How Naive Bayes Algorithm Works? (with example and full code)
- ↑
^{23.0}^{23.1}Bayes Classifier - an overview - ↑
^{24.0}^{24.1}^{24.2}^{24.3}Naive Bayes classifier - ↑ A practical explanation of a Naive Bayes classifier
- ↑ Naive Bayes Classifier
- ↑
^{27.0}^{27.1}^{27.2}^{27.3}Naive Bayes Classifier in Machine Learning - ↑ Naive Bayesian
- ↑
^{29.0}^{29.1}^{29.2}Multinomial Naive Bayes Classifier Algorithm - ↑ Naive Bayes Classifier in Python Using Scikit-learn
- ↑ Classification Algorithms
- ↑
^{32.0}^{32.1}^{32.2}^{32.3}Using a naive Bayesian classifier methodology for loan risk assessment: Evidence from a Tunisian commercial bank - ↑
^{33.0}^{33.1}^{33.2}^{33.3}The Naive Bayes Algorithm in Python with Scikit-Learn - ↑
^{34.0}^{34.1}Naive Bayes Classification using Scikit-learn - ↑ A Bayesian Classifier Learning Algorithm Based on Optimization Model

## 메타데이터

### 위키데이터

- ID : Q812530

### Spacy 패턴 목록

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