Once you have built a machine learning model for a business, the first question that will strike your mind is—how successful is this model? There are three key parameters to measure the performance of your ML model:
- Accuracy
- Precision
- Recall
Keep reading to learn about machine learning accuracy, and how you can ensure and improve it in your model.
What Is Accuracy in Machine Learning?
This evaluation metric defines the percentage of correct predictions made by a specific model based on the data input. The proper way to measure is by dividing the number of correct predictions by the total number of predictions.
Accuracy is calculated in the following manner in terms of binary classification:
Accuracy = True Positive + True Negative / True Positive + True Negative + False Positive + False Negative
How Can You Ensure and Improve Accuracy in Your ML Model?
As confirmed by industry leaders, if you are working on a classification problem, your best score would be 100% Accuracy! However, this score is impossible to achieve for any predictive model. Therefore, prepare yourself to expect errors.
Nonetheless, you can always choose a platform that follows the best practices to ensure accuracy and work towards improving your system. The steps they follow are:
Revise the Problem
When you are not happy with your initial machine learning Accuracy answers, try reframing the problem once. Try to have a different perspective this time, you might have been asking the wrong questions.
Feed-in More Data
While some are against overfitting the training set with excessive data, you might need to provide more data samples while testing your ML model’s Accuracy level.
The ideal data feed ratio would be:
- 60% of data for training
- 20% of data for validating
- 20% of data for testing
Read More: How Machine Learning Works
Understand Data Features
Feeding in more data is not bereft of its disadvantages, like creating a lot of noise. It’s important to use meaningful data that are in line with the problem. Permutation Feature Importance or PFI can help you judge between irrelevant and sensible data features and ensure more accuracy with less clutter.
Add Contextual Data
Ensuring accuracy in a model has a lot to do with context. For instance, a house in California—has not enough data context compared to—a house in California specifying the neighborhood details like average income, age, security standards, etc.
Split & Validate
Known as cross-validation, this technique will improve your model’s robustness by first splitting the data into several partitions and then training multiple algorithms there.
Choose an Alternative Algorithm
You must alter your path, for your data structures might not be suitable for the problem you’re trying to solve in your model. For better accuracy, consider a different algorithm.
Although accuracy is regarded as the fraction of predictions to determine a model’s success rate, it mitigates error cost for businesses when optimized. It is accuracy in a business model that plays a key role in critical decision making. But apart from that, machine learning Accuracy assures less flawed, more organized, and comprehensive operational models for businesses.
