Today we are into digital age, every 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 exponential growth in data from people and & internet of things, a key to survival is to use machine learning & make that data more meaningful, more relevant to enrich 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 model and ensure it produces better results.
· Ensure that you have variety of data that covers almost all the scenarios and not biased to any situation. There was a news in early pokemon go days that it was showing only white neighborhoods. It’s because the creators of the algorithms failed to provide a diverse training set, and didn't spend time in these neighborhoods. Instead of working on a limited data, ask for more data. That will improve the accuracy of the model.
· Several times the data received has missing values. Data scientists have to treat outliers and missing values properly to increase the accuracy. There are multiple methods to do that – impute mean, median or mode values in case of continuous variables and for categorical variables use a class. For outliers either delete them or perform some transformations.
· Finding the right variables or features which will have maximum impact on the outcome is one of the key aspect. This will come from better domain knowledge, visualizations. It’s imperative to consider as many relevant variables and potential outcomes as possible prior to deploying a machine learning algorithm.
· Ensemble models is combining multiple models to improve the accuracy using bagging, boosting. This ensembling can improve the predictive performance more than any single model. Random forests are used many times for ensembling.
· 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.
There are some more ways but the ones mentioned above are foundational steps to ensure model accuracy.