Data validation is the process of assessing the quality and accuracy of data used for artificial intelligence (AI) model training. The goal of data validation is to make sure that the data is suitable for use in the training process and to prevent model errors or prejudice.
This process includes several steps.
Before proceeding with data validation, it is important to divide the data into the training and validation sets. Training data is used for AI model training, and the validation data is used to assess the model performance.
Missing values can have a negative impact on the AI model performance. It is important to check the missing values and choose the best way to handle them, e.g. complementing the missing values or deleting samples that contain missing values or are duplicated.
Deviating values are data points that are significantly different than other data points. It is important to check the deviating values and choose the best way to handle them, such as deleting the deviating values or transforming the data.
Data balance refers to data distribution among various classes. A data set with an unbalanced distribution can have a negative impact on the AI model performance. It is important to check the data balance and choose the best way to handle it, such as oversampling for the minority class or undersampling for the majority class.
Use a validation set to assess model performance on data not seen before. It helps to determine whether the model is overfitting, underfitting or well-generalising for new data in general.
Data quality can significantly affect the AI model performance. It is important to check the data quality and choose the best way to handle it, such as identifying errors or checking duplicates.
Building on the assessment results, in order to improve performance it is necessary to optimise the model through the adaptation of its parameters or architecture.
data validation
Data validation is an important stage in the process of artificial intelligence (AI) training because it helps to make sure that the model is resistant and may generalise results on new data. Through constant monitoring and adaptation of results during the training process, you can make sure that the model learns effectively and predicts results with high accuracy. We can easily provide you with qualified native-speaker validators who will ensure that your data improve the performance and quality of your AI.
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