Every artificial intelligence (AI) project aims to create a model with higher accuracy in the outcome. High-quality training data is at the center of the efforts to improve an AI project’s modeling algorithms and fine-tuning parameters. Even the best machine learning (ML) model will produce inconsistent outcomes if you input data with poor quality.
Your data could have anomalies in data labeling—which you can effectively clean with a good annotation tool. These anomalies may include duplicates, incorrect entries, among other issues. Fortunately, there are various measures you can take to improve the data quality of your AI training models and achieve more accurate predictions. This article serves as a simple guide on the steps to take to make your AI training data more viable.
What is AI training data?
AI uses a dataset of labeled video, audio, images, and other data types for training algorithms. However, data in the training model needs to be error-free and correctly entered to create an intelligent algorithm. Any form of error can compromise the integrity of your datasets, hence, the thorough observation of outcomes.
High-quality AI training data is well-labeled, consistent, accurate, complete, and valid in representing the problem you’re trying to solve with your model. Any data that could mislead an ML algorithm is of poor quality and can lead to low performance and AI bias. Here’s how to improve data quality.
1. Eliminate duplicate and irrelevant observations for consistency
Clean from your dataset any data you deem irrelevant, such as duplicates. During data gathering, there is a high probability of several duplications, mainly when acquired from multiple sources. Thus, one of the essential aspects of improving your models’ data quality is eliminating duplicates.
Such data leads to irrelevant observations that occur when the outcome of your project has no bearing on the problem you’re trying to solve. For example, if you wish to examine data on millennial clients, but your dataset includes observations from previous generations, you might want to remove the irrelevant set. In doing this, you’re creating an efficient and distraction-free dataset that produces more accurate outcomes.
2. Fix structural anomalies to improve accuracy
Structure anomalies occur when you have data with naming issues, capitalization, typographical errors, and other issues that can cause errors in the data structure. Such inconsistencies can lead to mislabeled data classes or categories. You should ensure that data are labeled differently but mean the same thing and analyzed in the same class.
An excellent example is data labeled ‘Not Applicable’ and ‘N/A.’. Designating the two into different categories can result in inconsistencies. They’re both in the same category, and you should treat them as such.
3. Validate the outliers
It’s common to find observations that look off and don’t seem to fit with the data you’re analyzing. You can improve the quality of your AI training data by simply removing such an outlier if you have a good reason to do so, such as incorrect data entry.
However, it would be best to exercise caution when filtering outliers as it could be the key to proving your theory. That means you should not assume that an outlier is inaccurate just because it exists. The best approach here is first to validate the accuracy of a particular outlier to determine if it’s a mistake or is unrelated to the analysis.
4. Deal with missing data for completeness
Missing or incomplete information is detrimental to the successful training of your ML projects as many algorithms will reject anything with missing values or those that make incorrect assumptions. These assumptions result in erroneous outcomes. There are a few ways you can consider handling missing AI training data:
- You can consider dropping all observations with missing values. However, take a lot of caution because this means losing a portion of your data. You don’t want to lose other valuable data in the process.
- You can try to input the missing values from other observations, but this can also lead to lower accuracy because you’ll be relying on assumptions instead of factual data.
- Lastly, you might need to alter how the training model uses data to navigate the missing values.
5. Carry out Quality Assurance (QA) testing
At the end of the day, you should be able to answer the following questions:
- Is the information logical?
- Is the data consistent with its field’s standards?
- Are there any new insights that you can draw from this information?
- Is there a pattern in the data that can help you build a new hypothesis?
- If not, is this due to a problem with the data quality?
Takeaway
Your AI training model and decision-making will suffer by drawing the wrong inferences from poor quality or inaccurate data. Working with flawed data will produce inadequate outcomes and waste your resources and time fixing errors. The most important element in AI training data is its quality. Establish a culture of collecting high-quality data and carrying out regular data cleaning.
