Machine Learning (ML) model accuracy is the most important factor that makes such developments successful and reliable in the market. The more accurate the model, it will give the precise results in various scenarios making such a model more meaningful and relevant to enrich the customer experience when used in real life.
If your ML model is not giving satisfying results, you need to work closely to improve the accuracy. There are various ways you can improve accuracy of machine learning model which is discussed below to get better results.
Five Ways to Increase Accuracy Of Machine Learning Model
Feed with More Training Data
Having more amount of data means you are giving more information to your machine learning algorithms to understand the various situations and correlate the same before giving the right answer.
And having more training data means, you need to add a variety of data that can cover wide-ranging scenarios to avoid biased decisions. Hence, the more data you feed it will improve the accuracy of the model.
Treat the Missing Values in Data
The missing and outlier values in the machine learning training data also reduce the accuracy of the model or makes the model give a biased decision. It happens because usually, we don’t analyze the action and relationship with other variables in the right way.
There are multiple methods to treat outliers and missing values – impute mean, median or mode values in the case of continuous variables, and for categorical variables use a class. For outliers, you can either delete them or do some transformations.
Finding the Right Variables or Features
Another way to improve the ML model accuracy is finding the right variables or features which is extracted as new information. Features have a maximum impact on the outcome and are one of the key aspects.
If you have better knowledge and visualizations, you can find the variables correctly. And it is vitally important to consider as many relevant variables and potential outcomes as possible before deploying a machine learning algorithm.
Ensemble Models Method
Ensemble models are the most common method that combines multiple models to improve accuracy using bagging and boosting. The Ensemble method can improve the performance of prediction of more than any single model. And random forests is the technique used many times for assembling the machine learning model.
There are two reasons to apply ensemble methods to improve the accuracy of your model. First – they are generally more complex than traditional methods and second – The traditional methods give the right base level from which you can improve and draw to create your ensembles for your ML model.
Re-validation of Model
The accuracy of machine learning model can be also improved by re-validating the model at regular intervals. For this, it is important to score the model after using the new data on a daily, weekly, or monthly basis as per the changes in the data.
And if necessary, rebuild the models at periodic levels with different types of techniques to challenge the model present in the production. ML model validation can be done automatically or manually, depending on the type of model and validation team available for performing such task in an unbiased manner.
Apart from these popular methods, using multiple algorithms and tunning the algorithms to find the optimum value for each parameter also improves the accuracy of the model. However, higher accuracy models don’t need to always give accurate results, as sometimes, the improvement in the model’s accuracy can be due to over-fitting too. But you can follow the mentioned above foundational methods to ensure your model’s accuracy.
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