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Nov 4, 2018 · Learn how Naive Bayes, a probabilistic machine learning algorithm, works with a simple example and full code in R and Python. Understand the conditional probability, Bayes rule, Laplace correction and Gaussian Naive Bayes.
- Selva Prabhakaran
- What Is Naive Bayes Classifiers?
- Why It Is called Naive Bayes?
- Assumption of Naive Bayes
- Bayes’ Theorem
- Advantages of Naive Bayes Classifier
- Disadvantages of Naive Bayes Classifier
- Applications of Naive Bayes Classifier
- Conclusion
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. To start with, let us consider a dataset. One of the most simple and effective c...
The “Naive” part of the name indicates the simplifying assumption made by the Naïve Bayes classifier. The classifier assumes that the features used to describe an observation are conditionally independent, given the class label. The “Bayes” part of the name refers to Reverend Thomas Bayes, an 18th-century statistician and theologian who formulated ...
The fundamental Naive Bayes assumption is that each feature makes an: 1. Feature independence:The features of the data are conditionally independent of each other, given the class label. 2. Continuous features are normally distributed:If a feature is continuous, then it is assumed to be normally distributed within each class. 3. Discrete features h...
Bayes’ Theorem finds the probability of an event occurring given the probability of another event that has already occurred. Bayes’ theorem is stated mathematically as the following equation: where A and B are events and P(B) ≠ 0 1. Basically, we are trying to find probability of event A, given the event B is true. Event B is also termed as evidenc...
Easy to implement and computationally efficient.Effective in cases with a large number of features.Performs well even with limited training data.It performs well in the presence of categorical features.Assumes that features are independent, which may not always hold in real-world data.Can be influenced by irrelevant attributes.May assign zero probability to unseen events, leading to poor generalization.Spam Email Filtering: Classifies emails as spam or non-spam based on features.Text Classification: Used in sentiment analysis, document categorization, and topic classification.Medical Diagnosis:Helps in predicting the likelihood of a disease based on symptoms.Credit Scoring:Evaluates creditworthiness of individuals for loan approval.In conclusion, Naive Bayes classifiers, despite their simplified assumptions, prove effective in various applications, showcasing notable performance in document classification and spam filtering. Their efficiency, speed, and ability to work with limited data make them valuable in real-world scenarios, compensating for their naive independence assu...
- 17 min
Naïve Bayes Classifier Algorithm. Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems. It is mainly used in text classification that includes a high-dimensional training dataset.
May 23, 2024 · Learn how to use the Naive Bayes algorithm, a fast and simple classification technique based on Bayes' theorem, for text classification, spam filtering and more. See a practical example of applying the algorithm to a HR analytics problem and compare it with other methods.
Jan 16, 2021 · Learn the concept and steps of the Naive Bayes algorithm, a machine learning technique for classification based on Bayes theorem. See an example of how to use Naive Bayes to predict whether a person walks or drives to work based on salary and age.
May 3, 2024 · Learn the mathematical intuition and implementation of Naive Bayes Classifiers, a probabilistic machine learning model for classification problems. See an example of how to use Bayes' rule and conditional probability to predict whether you can pet an animal or not.
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Nov 3, 2020 · Naive Bayes Classifiers (NBC) are simple yet powerful Machine Learning algorithms. They are based on conditional probability and Bayes's Theorem. In this post, I explain "the trick" behind NBC and I'll give you an example that we can use to solve a classification problem.
