Since the coronavirus pandemic started, many companies have had difficulties including the IT industry. Unnecessary waste of managing businesses was created mainly because of the transition to remote work. Nevertheless, data science and machine learning still show that expansion is almost limitless.
Moreover, machine learning and its algorithms discover insights which are based on data from the real world and can be used to predict the future. When new data becomes available machine learning programs adapt and produce new predictions. However, there are still some occasions when technology outperforms linear and statistical algorithms. Below you will see a list of the three most common situations where machine learning has a big influence:
Engineers experiencing problems when coding rules
Human-oriented tasks such as recognising spam emails are rule-based solutions. Engineers have to write and update many lines of code whenever a variety of factors influence an answer. When too many factors are influencing the rules, it becomes very difficult for humans to make precise coding rules. Machine learning programs are free to encode actual patterns and only need algorithms to extract patterns automatically.
A solution for millions of cases
Thousands of payments can be categorised as fraudulent or not, but this can be very tedious or even impossible in some cases when dealing with millions of transactions. When user bases becomes larger it is no longer possible to process payments by hand. Machine learning provides an effective solution at handling types of large-scale problems with no human intervention.
Possible, but not cost-efficient
Some situations could process very quickly yet at a high cost. When a person assesses DMV forms for in-state and cross-state car purchases to determine the validity the business processes are well-defined and may take only a few minutes to check the forms separately. On the other hand, allocating so much manual labor would definitely not be budget-friendly. This is when machine learning could offer pay-as-you-go pricing for fully scaled operations.
Overall, it is of significant importance to bear in mind that machine learning is just tool. Machine learning models provide advanced algorithms which identify patterns in data and cases which are properly applied to the right use can reduce the amount of time spent on IT operations. This adds significant business value and reduces the costs.
Source: https://www.provintl.com/blog/5-common-machine-learning-problems-how-to-beat-them
I wonder how this would increase bias? I also wonder if this would even increase overall dataset variance?