Naive Bayes Algorithm Overview

A classification approach where the model learns from labeled data, making predictions based on observed patterns.

A classification approach where the model relies on prior knowledge or assumptions, without training on specific examples.

Attributes that influence the output variable. They are used to predict the value of the output attribute.

The attribute that represents the output of the classification model. It's what we're trying to predict.

Numerical attributes, where the values can be measured and ordered.

Attributes that fall into categories or groups, without an inherent order.

The process of building and evaluating a classification model. It involves training the model on labeled data and then testing its accuracy on unseen data.

The data used to train the classification model. It includes labeled examples of both input attributes and the corresponding output attribute.

The Naive Bayes algorithm calculates the probability of an event based on prior knowledge and observed evidence.

In the training phase, the algorithm calculates the probability of each class (e.g., spam or not spam) and the likelihood of each feature occurring within each class.

In the prediction phase, the algorithm uses Bayes' Theorem to calculate the probability of a new instance belonging to each class based on its features. The class with the highest probability is predicted.

Naive Bayes is a simple and efficient algorithm that works well with large datasets and requires less data compared to some other methods.

Naive Bayes assumes features are independent of each other, which may not be true in real-world scenarios. Additionally, it can struggle with rare categories, leading to inaccurate predictions.

A measure of how much uncertainty is reduced when we know the value of a specific attribute in a dataset.

The probability of an event occurring given that another event has already occurred.

A measure of impurity in a dataset, indicating how mixed the classes are within a subset of data.

A table that shows the conditional probability of each possible value of a variable given the values of other variables.

Directly related to the information content and uncertainty in the data, providing insights into how much information an attribute contributes to reducing uncertainty.

The process of using a model to predict the outcome of a new event.

A rule used to determine the most likely outcome given a set of evidence.

It can be biased towards attributes with many values, as these attributes can lead to a larger reduction in entropy or impurity.

It's generally more computationally intensive compared to the Gini Index, as it involves calculating entropy and information gain for each attribute.

The process of using training data to build a model that can be used to predict outcomes in the future.

Often preferred for algorithms like ID3 and C4.5 that rely on minimizing uncertainty or maximizing information gain.

It's less intuitive compared to information gain, as it focuses on the purity of classes rather than directly measuring the reduction of uncertainty.

It can also be biased towards attributes with many values, as these attributes often lead to more homogeneous subsets with higher purity.

A classification algorithm that uses Bayes' Theorem to calculate the probability of an event based on prior knowledge of related conditions.

A statistical theorem that describes the probability of an event happening given the occurrence of another related event.

The assumption that features used for classification are independent of each other, meaning that the value of one feature doesn't affect another feature.

A decision boundary in a Naive Bayes classifier involves only one feature at a time. This means the algorithm considers only one variable when making a decision.

A type of Naive Bayes classifier that assumes features follow a normal distribution. Ideal for continuous data, like measurements or numerical values.

A type of Naive Bayes classifier used for discrete counts. It works best for situations where data represents the frequency of occurrences, like text documents.

A type of Naive Bayes classifier that handles binary features, either 0 or 1. Useful for text classification where presence or absence of specific words matters.

The ability of a machine learning model to learn from data and make predictions on new, unseen data.

The model correctly identifies a positive case.

The model incorrectly identifies a negative case as positive.

The model correctly identifies a negative case.

The model incorrectly identifies a positive case as negative.

Measures how many of the positive predictions are actually correct.

Measures how many of the actual positive cases are correctly identified.

Combines precision and recall into a single metric.

A table that visualizes the performance of a classifier.