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4 types of classification tasks in machine learning

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4 types of classification tasks in machine learning

Classification is an undertaking that requires the utilization of machine learning algorithms that figure out how to relegate a class name to models from the difficult area. A simple model is grouping emails as “spam” or “not spam.”

There are perhaps four main types of classification tasks that you may encounter; they are:

  • Binary Classification
  • Multi-Class Classification
  • Multi-Label Classification
  • Imbalanced Classification

Binary Machine learning Classification 

Binary classification refers to those classification tasks that have two class labels.

Examples include:

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  • Email spam detection (spam or not).
  • Churn prediction (churn or not).
  • Conversion prediction (buy or not).

Typically, binary classification tasks involve one class that is the normal state and another class that is the abnormal state. To know the difference between deep learning and machine learning.

Multi-Class Machine learning Classification 

Multi-class classification refers to those classification tasks that have more than two class labels.

Examples include:

  • Face classification.
  • Plant species classification.
  • Optical character recognition.

Unlike binary classification, the multi-class classification does not have the notion of normal and abnormal outcomes. Instead, examples are classified as belonging to one among a range of known classes. To know the visual introduction of machine learning.

Multi-Label Machine learning Classification 

Multi-label classification refers to those order tasks that have at least two class labels, where at least one class names might be predicted for every model. Think about the case of photograph order, where a given photograph may have different items in the scene and a model may anticipate the presence of numerous known articles in the photograph, for example, “bike,” “an apple,” “individual,” and so on.

Imbalanced Machine learning Classification 

Imbalanced classification alludes to classification tasks where the quantity of models in each class is inconsistently conveyed. Commonly, imbalanced classification is twofold characterization undertakings where most of the models in the training dataset have a place with the typical class and a minority of models have a place with the anomalous class.

Examples include:

  • Fraud detection.
  • Outlier detection.
  • Medical diagnostic tests.

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