Machine learning models have the capability of handling complex data sets. Unfortunately, training them is easier said than done.
Such models require lots of training data. But building that data can be time-consuming and expensive.
The ideal machine learning model would have a perfect dataset with perfectly labeled data. But in reality, most data sets can be a mess. Some labels may be missing. Some labels may overlap. And a few may not be relevant to the task at hand.
And it’s more likely that some of these data quality issues will cause you headaches.
To confirm that the machine learning model is reliable, data scientists and engineers need to ensure the quality of training data.
This blog will look at various factors that determine the data training quality of a machine learning model and the best ways to improve quality assurance.
Determining the Quality with Accuracy and Consistency
Before putting a data model to real-world use, data scientists need to determine the quality of data.
Data scientists usually look at two aspects of data to assess its quality — accuracy, and consistency.
We can determine how consistent
a model is by noting how much a data annotation is in line with the others.
With consistency, we ask questions like,
- How often are the results of label annotations close to each other?
- Is there any noise between the labels?
But consistency alone isn't enough to determine the quality of data.
Even when labels are consistent, we must ensure that the results are close to real-world scenarios. And this is where accuracy
- How much closer is the data to the actual truth?
- What's the disparity between the training data and the reality?
Such questions will help us determine how accurate our annotations are.
We use several techniques like consensus algorithms, benchmarks, and reviews to assess a model's consistency and accuracy.
Measuring the Training Data Quality for Machine Learning
A machine learning algorithm can estimate the probability of some event, such as the likelihood of a user buying a product in an online store. But the learning can be complicated since it involves feeding the algorithms examples of real-world behavior that can fluctuate constantly.
You can use the following best practices and methods to assess the training quality and obtain an outcome closer to a real-world scenario.
Benchmark is used to determine the data quality in terms of its accuracy.
This method establishes a benchmark — a reference point
— decided by data experts after extensive research and consideration.
We compare every dataset or individual point in a set with this benchmark to check the data quality.
Data scientists usually create a new benchmark with a label, compare the data quality at random intervals, and check for deviations.
This process of benchmarking the data differs from project to project. For example, there can be several ambiguities in identifying objects with similar structures in a visual interpretation algorithm. Here, data scientists need to visually and numerically assess the data and set the proper benchmark for accurate evaluation.
Consensus is for measuring the consistency between datasets in a model. We can calculate the consensus by adding all the labels that agree and dividing them by the total number of labels.
Consensus score = (sum of labels that are consistent) / total number of labels
The ideal situation here is for every data label to agree with each other. But there is bound to be some disagreement in a model.
These points of disagreeability need to be studied closely to understand the underlying reasons for the disparity and identify ways to arrive at a consensus.
We use Review
to assess the accuracy of the training data. Reviewing the datasets often can create a tight feedback loop to reiterate and improve the machine learning models.
Most reviewing processes happen manually — with visual verification.
The data scientists and engineers will pick a few labels and review each of them manually. They can also make edits to improve the accuracy and note down how reliable the overall model is.
Train Your Machine Learning Model with High-Quality Data
To get the best results from machine learning models, you need to train them with high-quality data.
However, getting the correct data to train the machine learning models is easier said than done. The data may not always be available, and sometimes, it may not be in a format that's compatible with your ML models.
Preparing training data for machine learning is challenging. It requires going through thousands of images, hours of customer interviews, or months of financial documents. It involves a team of people with different skills to lay the data out and organize it consistently.
Our team of data experts at Traindata can help you collect and train your machine learning models and achieve high accuracy and consistency. We have an experienced team of over 15 years in data annotation and labeling.
Please email us at [email protected]
to discuss how we can help you achieve high data training quality.