By Prangya Pandab - January 13, 2021 4 Mins Read
Successful machine learning initiatives can add value to the organization by providing insights to the right person/system at the right time within the right context. Therefore, it is only as good as the data available, and that is why it needs new, updated data to deliver the most accurate outputs and predictions for any given need
The endless quantity of available and affordable data storage and the growth of low-cost, powerful processing have propelled the growth of ML. It is helping computers tackle tasks that have, until now, only been carried out by people. Industries are now developing more robust AI to analyze bigger and more complex data while delivering faster and accurate results on a vast scale.
But, advances in innovations that capture and process abundant data, have left everyone drowning in information. It becomes hard to extract insights from data, and this is where machine learning offers some benefit to businesses.
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Working hard in the wrong direction will only result in businesses burning through a lot of time and no progress. Therefore, companies need effective and efficient strategies to improve machine learning performance.
Boosting Machine learning is data-intensive; hence, the quality of the data used in a machine learning project will have a massive effect on its chances for success. Because of the massive volume of data required, even small errors in the training data can lead to large scale errors in the output.
Poor data quality is the biggest to the profitable, widespread use of machine learning. The quality demands of machine learning are high, and bad data can rear its ugly head multiple times, both in the new data used by the model to make future decisions, and the historical data for training the predictive model.
For organizations to have the right data for machine learning,
There is no one solution fits all approach and multiple factors need to be taken into consideration while choosing to boost machine learning algorithm.
While some problems are very specific and require a unique approach, others are very open and need a trial and error approach. Supervised learning, classification, and regression are very open. It can be used in anomaly detection or to build more general sorts of predictive models.
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Furthermore, some of the decisions that organizations make while choosing a machine learning algorithm have less to do with the technical aspects of the algorithm or the organization but more to do with business decisions.
Just building a machine learning model is not enough to get the right predictions; there needs to be a constant check on the accuracy and the validity to ensure businesses get the precise results.
There are a variety of validation techniques that can be followed, but businesses need to make sure they choose the best one that is suitable for their ML model validation. Then it will help them improve the overall performance in an unbiased manner.
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A large portion of all machine learning models are created on a static subset of information, and they capture the conditions of the time frame when the data was gathered. They tend to become dated over time and give less accurate results. Businesses should regularly replace, update, and retrain the models depending upon how frequently the business climate changes.
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Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.
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