Machine Learning: Drawbacks and Limitations

Machine Learning: Drawbacks and Limitations

Machine learning has profound applications due to its notable advantages. However, it also has several disadvantages or drawbacks and limitations.

Challenges: The 5 Major Drawbacks and Limitations of Machine Learning

1. A key disadvantage of machine learning involves long-term and continuous exposure to large volumes of data. The technology is not readily deployable. For it to make predictions o decisions, it needs to learn through data exposure.

2. Identifying and resolving errors is another drawback. Doing so requires specialized expertise and familiarity with the specific machine learning system because it involves analyzing complex algorithms and processes.

3. Another limitation  is its sole dependence on historical data. It cannot make absolute predictions or decisions simply because there are situations that cannot be analyzed with historical data alone.

4. Deploying a capable machine learning system also requires investment and utilization of expensive computer resources. What this means is that machine learning is not readily usable for small organizations. Furthermore, its capability is dependent on the capabilities of its underlying hardware and software components.

5. Other disadvantages and limitations include an inability to understand context, susceptibility to unintended or hidden biases depending on input data, lack of capability to pose questions, and inability to provide rational reasons for its predictions or decisions.

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