Common issues with ML

With the use of machine learning (ML), which is a form of artificial intelligence (AI), software programs may anticipate outcomes more accurately without having to be explicitly instructed to do so. To forecast new output values, machine learning algorithms use past data as input.

Despite being applied in multiple industries, machine learning still has a lot of issues that cannot be overlooked. Here are some of them:

 1. Inadequate data

The most common issue is the lack of quantity or the quality of the data used to train the model. While simple tasks may require only a few thousand sample data, for a more advanced task like image recognition you may need millions of samples. Regarding the data quality it can have the following problems:

• noisy data (any data that cannot be understood and interpreted correctly by machines)
• incorrect data (caused by human error for example)
• generalizing of output data (becomes way too complex to generalize)

2. Overfitting

A machine learning model begins collecting noise and erroneous data into the training data set once it is trained with a large amount of data. As a result, the model's performance suffers. Let’s take an example of a training data set where we have 1000 apples, 1000 bananas and 8000 papayas. The chances that the model will identify a lot of apples as papayas are pretty high because there is a massive amount of biased data in the training data set. Overfitting usually happens due to the usage of non-linear methods. This problem can be solved by using linear and parametric algorithms. Although there are several other methods to reduce overfitting:

•  increasing the training data
•  early stopping during the training phase
•  reduce the noise in the data set

 3. Underfitting

Underfitting is the exact opposite of overfitting. It occurs when we have limited data and try to build a linear model with non-linear data. Some methods to reduce underfitting:

•  increase the number of features
•  increase the number of epochs
•  reduce the noise in the data set

 4. Lack of explainability

Machine learning models suffer from a lack of explainability. This implies that the results get increasingly difficult to comprehend as time goes on. It becomes very hard to reverse-engineer a machine-learning model after some time, decreasing its validity. Unfortunately, sophisticated machine learning techniques don't offer the required transparency or clarity.

 5. Slow implementation

Although machine learning models are quite effective at producing accurate predictions, sometimes it takes a long time. The most common cases are due to slow programs, data overload, and excessive requirements. To get the best results, it also needs ongoing maintenance and monitoring.

Bibliography:

https://www.geeksforgeeks.org/7-major-challenges-faced-by-machine-learning-professionals/

https://www.javatpoint.com/issues-in-machine-learning

https://www.hyperon.io/blog/common-problems-with-machine-learning-that-companies-face