In a world where many m anual operations are mechanized, the definition of the word ‘manual’ is evolving. Computers can play chess, perform surgery, and develop into smarter, more humanlike machines with the aid of machine learning algorithms. Want to know more about NAIVE BAYES ALGORITHM visit here.. https://www.rangtech.com/blog/naive-bayes-algorithm
It is a classification technique based on Bayes’ Theorem with an independence assumption among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
The Naïve Bayes classifier is a popular supervised machine learning algorithm used for classification tasks such as text classification. It belongs to the family of generative learning algorithms, which means that it models the distribution of inputs for a given class or category. This approach is based on the assumption that the features of the input data are conditionally independent given the class, allowing the algorithm to make predictions quickly and accurately.
In statistics, naive Bayes classifiers are considered as simple probabilistic classifiers that apply Bayes’ theorem. This theorem is based on the probability of a hypothesis, given the data and some prior knowledge. The naive Bayes classifier assumes that all features in the input data are independent of each other, which is often not true in real-world scenarios. However, despite this simplifying assumption, the naive Bayes classifier is widely used because of its efficiency and good performance in many real-world applications.
Moreover, it is worth noting that naive Bayes classifiers are among the simplest Bayesian network models, yet they can achieve high accuracy levels when coupled with kernel density estimation. This technique involves using a kernel function to estimate the probability density function of the input data, allowing the classifier to improve its performance in complex scenarios where the data distribution is not well-defined. As a result, the naive Bayes classifier is a powerful tool in machine learning, particularly in text classification, spam filtering, and sentiment analysis, among others.
For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.
An NB model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.
This describes the supervised machine learning, supervised learning categorisation( regression and classification) and their types, applications of supervised machine learning, etc.
This presentation inludes step-by step tutorial by including the screen recordings to learn Rapid Miner.It also includes the step-step-step procedure to use the most interesting features -Turbo Prep and Auto Model.
In a world where many m anual operations are mechanized, the definition of the word ‘manual’ is evolving. Computers can play chess, perform surgery, and develop into smarter, more humanlike machines with the aid of machine learning algorithms. Want to know more about NAIVE BAYES ALGORITHM visit here.. https://www.rangtech.com/blog/naive-bayes-algorithm
It is a classification technique based on Bayes’ Theorem with an independence assumption among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
The Naïve Bayes classifier is a popular supervised machine learning algorithm used for classification tasks such as text classification. It belongs to the family of generative learning algorithms, which means that it models the distribution of inputs for a given class or category. This approach is based on the assumption that the features of the input data are conditionally independent given the class, allowing the algorithm to make predictions quickly and accurately.
In statistics, naive Bayes classifiers are considered as simple probabilistic classifiers that apply Bayes’ theorem. This theorem is based on the probability of a hypothesis, given the data and some prior knowledge. The naive Bayes classifier assumes that all features in the input data are independent of each other, which is often not true in real-world scenarios. However, despite this simplifying assumption, the naive Bayes classifier is widely used because of its efficiency and good performance in many real-world applications.
Moreover, it is worth noting that naive Bayes classifiers are among the simplest Bayesian network models, yet they can achieve high accuracy levels when coupled with kernel density estimation. This technique involves using a kernel function to estimate the probability density function of the input data, allowing the classifier to improve its performance in complex scenarios where the data distribution is not well-defined. As a result, the naive Bayes classifier is a powerful tool in machine learning, particularly in text classification, spam filtering, and sentiment analysis, among others.
For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.
An NB model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.
This describes the supervised machine learning, supervised learning categorisation( regression and classification) and their types, applications of supervised machine learning, etc.
This presentation inludes step-by step tutorial by including the screen recordings to learn Rapid Miner.It also includes the step-step-step procedure to use the most interesting features -Turbo Prep and Auto Model.
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For #Enquiry:
Website: https://www.phdassistance.com/blog/a-simple-guide-to-assist-you-in-selecting-the-best-machine-learning-algorithm-for-business-strategy/
India: +91 91769 66446
Email: info@phdassistance.com
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Market driver analysis and product optimization are one of the central tasks in Product Marketing and thus relevant to virtually all types of businesses. BayesiaLab provides a uni!ed software platform, which can, based on consumer data,
1. provide deep understanding of the market preference structure
2. directly generate recommendations for prioritized product actions.
The proposed approach utilizes Probabilistic Structural Equation Models (PSEM), based on machine-learned Bayesian networks. PSEMs provide an ef!cient alternative to Structural Equation Models (SEM), which have been used traditionally in market research.
Top 10 Data Science Practitioner PitfallsSri Ambati
Top 10 Data Science Practitioner Pitfalls Meetup with Erin LeDell and Mark Landry on 09.09.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Machine Learning Algorithm for Business Strategy.pdfPhD Assistance
Many algorithms are based on the idea that classes can be divided along a straight line (or its higher-dimensional analog). Support vector machines and logistic regression are two examples.
For #Enquiry:
Website: https://www.phdassistance.com/blog/a-simple-guide-to-assist-you-in-selecting-the-best-machine-learning-algorithm-for-business-strategy/
India: +91 91769 66446
Email: info@phdassistance.com
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H2O World 2015 - Mark Landry
Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Big shadow test
Big-Shadow-Test Method is used to solve a large simultaneous problem as a sequence of smaller simultaneous problems.
Shadow tests are no regular tests; their items are always returned to the pool. They are only assembled to balance the selection of items between current and future tests. Because of their presence, they neutralize the greedy character inherent in sequential test-assembly methods. In doing so, they prevent the best items from being assigned only to earlier tests and keep the later test-assembly problems feasible.
Supervised learning is a machine learning approach that's defined by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately.
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OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
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If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
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2. INTRODUCTION
Naïve Bayes Algorithm is a probabilistic machine learning algorithm based on
Bayes Theorem. It is used for the solution of classification problems. It is a
quick and efficient algorithm.
P(A|B) = P(B|A) P(A)/ P(B)
3. APPLICATION DOMAINS
This algorithm is used for real time predictions.
Multi class predictions can be easily carried out with this algorithm.
Gmail uses Naïve Bayes Algorithm to filter out spam mails. It decides whether the mail is spam
or not.
This algorithm is used in classifying data categorically, ranking pages and indexing relevancy
scores.
Naïve Bayes Algorithm is also used in text classification. It classify tweets, posts, blogs and
pages automatically without going through them manually. It has high success rate as
compare to other algorithms.
It is also used in building process of recommendation systems. These systems uses machine
learning and data mining to predict recommendations for users.
One of the important application area of Naïve Bayes Algorithm is sentiment analysis.
4. DESCRIPTION
It is a classification technique which is based on Bayes Theorem. Naïve Bayes is
used particularly for large data sets. It is easy to build. Naïve Bayes is also called
Independence Bayes or Simple Bayes. Naïve Bayes uses probability theory to
classify data. When new data is introduced, the probability of an event can be
adjusted. Naïve Bayes is a family of ML algorithms which uses statistical
independence. As compare to complex Bayes Algorithms, this algorithm is easy
to write and can be executed more reliably.
7. ADVANTAGES AND DISADVANTAGES
ADVANTAGES
• Naïve Bayes Algorithm is very fast.
• Class of test dataset can be easily predicted by this algorithm.
• Naïve Bayes Algorithm is useful with multi class predictions.
• With less training data, Naïve Bayes performs better than other models.
• This algorithm can save a lot of time because it works very quickly.
• It is easy to evaluate the conditional probability so it can easily be implemented.
• Naïve Bayes can handle both continuous and discrete data.
• This algorithm is highly scalable.
• Naïve Bayes Algorithm is not sensitive working with irrelevant features.
8. ADVANTAGES AND DISADVANTAGES
DISADVANTAGES
• If test data contains categorical variable and that was not present in training dataset, zero probability will
be assigned to it and no predictions will be made. This is also called a ‘Zero Frequency’.
• This algorithm assumes that all predictors are independent which is impossible and this limits the
algorithm from real world use cases.
• In some cases, this algorithm’s estimations can be wrong so we should not take the outputs too
seriously.
9. REFERENCES
Sunil, (2017), 6 Easy Steps to Learn Naive Bayes Algorithm with codes in Python,
https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained
Singh Chauhan, Nagesh, (2022), Naïve Bayes Algorithm: Everything You Need to Know,
https://www.kdnuggets.com/2020/06/naive-bayes-algorithm-everything.html
Vadapalli, Pavan, (2020), Naive Bayes Classifier: Pros & Cons, Applications & Types Explained,
https://www.upgrad.com/blog/naive-bayes-classifier
Shah, Rajvi, (2021), Naïve Bayes Algorithm's Advantages and Disadvantages
https://www.kaggle.com/getting-started/225022
Kumar, Naresh, (2019), Advantages and Disadvantages of Naive Bayes in Machine Learning,
http://theprofessionalspoint.blogspot.com/2019/03/advantages-and-disadvantages-of-
naive.html
