Types of classification in Machine learning

What are the types of classification in Machine learning?

There are several types of 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 used in machine learning, including:

1. Binary Classification: 

Binary classification is a common task in machine learning where the goal is to classify data into one of two possible classes or categories. It involves training a model on a labeled dataset, where each data point is associated with a class label, typically represented as 0 or 1, positive or negative, or any other binary representation.

2. Multi-Class Classification: 

Multi-class classification is a machine learning task where the goal is to classify data into one of three or more possible classes or categories. It is an extension of binary classification, where the number of classes is greater than two.

3. Multi-Label Classification: 

Multi-label classification is a machine learning task where each data instance can be associated with multiple class labels simultaneously. Unlike binary or multi-class classification, which assigns a single label to each instance, multi-label classification allows for the prediction of multiple labels for a single instance.

4. Hierarchical Classification: 

Hierarchical classification, also known as hierarchical multi-label classification or hierarchical classification with class hierarchy, is a machine learning task where the classes or labels are organized in a hierarchical structure. This structure represents relationships and dependencies between classes, allowing for a more organized and granular classification system.

5. Probabilistic Classification: 

Probabilistic classification, also known as probabilistic modeling or probabilistic classification modeling, is a machine learning approach that assigns probabilities to each class label instead of making deterministic predictions. It provides a measure of uncertainty and allows for more nuanced decision-making.

6. Rule-Based Classification: 

Rule-based classification, also known as rule-based learning or rule-based classification modeling, is a machine learning approach that relies on explicitly defined rules to make predictions or classify instances. Instead of learning patterns and relationships from data, rule-based classifiers use predefined rules that are derived from human expertise or domain knowledge.

7. Bayesian Classification: 

Bayesian classification is a machine learning approach that applies the principles of Bayesian statistics to classify instances. It is based on Bayes' theorem, which provides a way to update probabilities based on new evidence. Bayesian classification models calculate the posterior probability of each class given the observed features and then assign the class label with the highest posterior probability.

8. Instance-Based Classification: 

Instance-based classification, also known as instance-based learning or lazy learning, is a machine learning approach where the classification of new instances is based on the similarity to existing labeled instances in the training data. Instead of explicitly constructing a general model, instance-based classifiers store the training instances and use them directly during the classification process.