Today we are well into the digital age, almost every major business is using big data and machine learning to effectively target users with messaging in a language they really understand and push offers, deals, and ads that appeal to them across a range of channels.
With the exponential growth in data from people and & the internet of things, a key to survival is to use machine learning & make that data more meaningful and relevant to enrich the customer experience.
Machine Learning can also wreak havoc on a business if improperly implemented. Before embracing this technology, enterprises should be aware of the ways machine learning can fall flat. Data scientists have to take extreme care while developing these machine learning models so that it generate right insights to be consumed by business.
Here are 5 ways to improve the accuracy & predictive ability of machine learning models and ensure they produce better results.
1. Ensure that you have a variety of data that covers almost all the scenarios and is not biased to any situation. There was news in the early Pokemon Go days that it was working only in white neighborhoods. It’s because the creators of the algorithms failed to provide a diverse training set, and didn't spend time in other neighborhoods. Instead of working on a limited data, ask for more data. That will improve the accuracy of the model.
2. The data received often has missing values. Data scientists have to treat outliers and missing values properly to increase accuracy. There are multiple methods to do that – impute mean, median or mode values in the case of continuous variables and for categorical variables use a class. For outliers either delete them or perform some transformations.
3. Finding the right variables or features which will have maximum impact on the outcome is one of the key aspects. This will come from better domain knowledge and visualizations. It’s imperative to consider as many relevant variables and potential outcomes as possible prior to deploying a machine learning algorithm.
4. Ensemble models are combining multiple models to improve the accuracy using bagging and boosting. This ensembling can improve the predictive performance more than any single model. Random forests are used many times for ensembling.
5. Re-validate the model at proper time frequency. It is necessary to score the model with new data every day, every week or month based on changes in the data. If required rebuild the models periodically with different techniques to challenge the model present in the production.
These are some more ways but the ones mentioned above are foundational steps to ensure model accuracy.
Machine learning puts the power in the hands of organizations but as mentioned in the Spider-Man movie – 'With great power, comes great responsibility' so use it properly.