Advantages and Disadvantages of different types of machine learning algorithms

Less than 500 views Posted On Aug. 16, 2020

Prerequisite: Different Types of Machine Learning Algorithms

The various advantages and disadvantages of different types of machine learning algorithms are -

Advantages of Supervised Machine Learning Algorithms

  • Classes represent the features on the ground.
  • Training data is reusable unless features change.

Disadvantages of Supervised Machine Learning Algorithms

  • Classes may not match spectral classes.
  • Varying consistency in classes.
  • Cost and time are involved in selecting training data.

Advantages of Unsupervised Machine Learning Algorithms

  • No previous knowledge of the image area is required.
  • The opportunity for human error is minimized.
  • It produces unique spectral classes.
  • Relatively easy and fast to carry out.

Disadvantages of Unsupervised Machine Learning Algorithms

  • The spectral classes do not necessarily represent the features on the ground.
  • It does not consider spatial relationships in the data.
  • It can take time to interpret the spectral classes.

Advantages of Semi-supervised Machine Learning Algorithms

  • It is easy to understand.
  • It reduces the amount of annotated data used.
  • It is a stable algorithm.
  • It is simple.
  • It has high efficiency.

Disadvantages of Semi-supervised Machine Learning Algorithms

  • Iteration results are not stable.
  • It is not applicable to network-level data.
  • It has low accuracy.

Advantages of Reinforcement Machine Learning Algorithms

  • Reinforcement Learning is used to solve complex problems that cannot be solved by conventional techniques.
  • This technique is preferred to achieve long-term results which are very difficult to achieve.
  • This learning model is very similar to the learning of human beings. Hence, it is close to achieving perfection.

Disadvantages of Reinforcement Machine Learning Algorithms

  • Too much reinforcement learning can lead to an overload of states which can diminish the results.
  • This algorithm is not preferable for solving simple problems.
  • This algorithm needs a lot of data and a lot of computation.
  • The curse of dimensionality limits reinforcement learning for real physical systems.
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