Advantages and Disadvantages of different types of machine learning algorithms

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.