Machine learning typically is used to solve a host of diverse problems within an organization, extracting predictive knowledge from both structured and unstructured data and using them to deliver value. The technology has already made its way into different aspects of a business ranging from finding data patterns to detect anomalies and making recommendations. Machine learning helps organizations gain a competitive edge by processing a voluminous amount of data and applying complex computations.
With machine learning, companies can develop better applications according to their business requirements. This technology is mainly designed to make everything programmatic. Applications of ML have the potential to drive business outcomes that can extensively affect a company’s bottom line. The rapid evolution of new techniques in recent years has further expanded the machine learning application to nearly boundless possibilities.
Industries relying on massive volumes of data are significantly leveraging ML techniques to process their data and to build models, strategize, and plan.
While implementing the effective application of machine learning enables businesses to grow, gain competitive advantage and prepare for the future, there are some key practical issues in ML and their business implications organizations must consider.
Data Quality and Noise
As machine learning significantly relies on data, the occurrence of noisy data can considerably impact any information prediction. Generally, data from a dataset carries extraneous and meaningless information which can significantly affect data analysis, clustering and association analysis. Having a lack of quality data can also restrain the capabilities of building ML models. In order to cope with quality data and noise, businesses need to apply better and effective machine learning strategies through data cleansing and overall processing of data.
There is no doubt that the development of machine learning has made it possible to learn directly from data rather than human knowledge with a strong emphasis on accuracy. However, the lack of the ability to explain or present data in understandable terms to a human, often called interpretability, is one of the biggest issues in machine learning. The introduction of possible biases in data has also led to ethical and legal issues with ML models. The interpretability levels in the field of machine learning and algorithms may significantly vary. Some methods are human-compatible as they are highly interpretable, while some are too complex to apprehend, thus require ad-hoc methods to gain an interpretation.
In the context of supervised machine learning, an imbalanced dataset often involves two or more classes. There is an imbalance among labels in the training data in several real-world datasets. This imbalance in a dataset has the potential to affect the choice of learning, the process of selecting algorithms, model evaluation and verification. The models can even suffer large biases, and the learning will not be effective if the right techniques are not employed properly. ML algorithms can generate insufficient classifiers when faced with imbalanced datasets. When trying to resolve certain business challenges with imbalanced data sets, the classifiers produced by standard ML algorithms might not deliver precise outcomes.
Thus, to address imbalanced datasets requires strategies like enhancing classification algorithms or balancing classes in the training data before providing the data as input to machine learning algorithms.
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