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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