[Also Read: What Is Training Data in Machine Learning]
Methods for Evaluating Training Data
To make the right data selection for your AI program, you must evaluate the right AI training data. This can be done by
- Identifying High-Quality Data with Enhanced Accuracy:
To identify good-quality data, you must ensure that the provided content is relevant to the application context. In addition, you need to figure out if the gathered data is redundant and valid. There are various standard quality tests that the data can be passed through, such as Cronbach’s alpha test, gold set method, etc., which can provide you with good quality data. - Leverage Tools for Evaluating Data Representatives and Diversity
As mentioned above, diversity in your data is the key to achieving the needed accuracy in your data model. There are tools that can generate detailed projections and track data results at a multi-dimensional level. This helps you identify if your AI model can distinguish between diverse data sets and provide the right outputs. - Evaluate Training Data Relevance
Training data must only contain attributes that provide meaningful information to your AI model. To ensure the right data selection, create a list of essential attributes your AI model should understand. Make the model familiar to those data sets and add those specific data sets to your data library.
How to Choose the Right Training Data for your AI Model?
It is evident that data is supreme when training your AI models. We discussed early in the blog how to find the right AI training data for your programs. Let us take a look at them:
- Data Defining: The first step is to define the type of data you need for your program. It segregates all the other data options and directs you in a single direction.
- Data Accumulation: Next is to gather the data that you are looking for and make multiple data sets from it which is relevant to your needs.
- Data Cleaning: Then the data is thoroughly cleaned, which involves practices like checking for duplicates, removal of outliers, fixing structural errors, and checking for missing data gaps.
- Data Labelling: Finally, the data that is useful for your AI model is labelled properly. Labelling reduces the risk of misinterpretation and provides better accuracy to the AI training model.
Apart from these practices, you must consider a few considerations when dealing with limited or biased training data. Biased data is AI-generated output based on erroneous assumptions that are false. There are ways like data augmentation and data markup that are incredibly helpful in reducing bias. These techniques are made for regularizing the data by adding slightly modified copies of existing data and improving the diversity of data sets.
[Also Read: How much is the optimum volume of training data you need for an AI project?]
Conclusion
AI training data is the most important aspect of a successful AI application. That is why it must be given utmost importance and significance while developing your AI program. Having the right AI training data ensures that your program can take many diverse inputs and still generate the right results. Reach out to our Shaip team to learn about AI training data and create high-quality AI data for your programs.
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