Training Parameters And Accuracy Measures

Our platform provides the flexibility to adjust a set of training parameters. There are general training parameters and advanced training options that could influence the model predictions. The predictions are measured on the basis of a set of accuracy measures or metrics that are also discussed in this section.

Training Options

Once you have fulfilled all the feature group requirements for the use case, you can set the following general and advanced training configuration options to train your ML model:

Training Option Name	Description	Possible Values
Name	The name you would like to give to the model that is going to be trained. The system generates a default name depending upon the name of the project the model is a part of.	The name can be comprised of any alphanumeric character and the length can be anywhere from 5 to 60 characters.
Image Table	Serves as input for vision models, incorporating image data and metadata to provide the essential structure required during the training of vision models.	Documents can be in formats like plain text, CSV, JSON, PDF, docx, or zip with IMAGE and DOCUMENT ID columns set
Set Refresh Schedule (UTC)	Refresh schedule refers to the schedule when your dataset is set to be replaced by an updated copy of the particular dataset in context from your storage bucket location. This value to be entered is a CRON time string that describes the schedule in UTC time zone.	A string in CRON Format. If you're unfamiliar with Cron Syntax, Crontab Guru can help translate the syntax back into natural language.

Metrics

Our AI engine will calculate the following metrics for this use case:

Metric Name	Description
Mean Average Precision (MAP)	MAP is defined as the relative relevance of a recommender system such that it rewards ranking the relevant recommendations higher and penalizes if they are ranked lower. It ranges from 0 to 1. Generally you can expect to see values ranging anywhere from 0.01 to 0.1 where higher values means a better score. In recommendation systems MAP computes the mean of the Average Precision (AP) over all the users. The AP is a measure that takes in a ranked list of recommendations and compares it to a list of the true set of "correct" or "relevant" recommendations for that user. AP rewards for having a lot of "correct" (relevant) recommendations in the list, and also rewards for putting the most likely correct recommendations at the top (penalizes more when incorrect guesses are higher up in the ranking). For more details, please visit this link.
mAP50	mAP50, or mean Average Precision at 50%, is a performance metric used in object detection tasks that quantifies the average precision of correctly identified and located objects within an image when considering the top 50% of predictions ranked by confidence scores.
Precision	Precision is the percentage of your results which are relevant. In other words, it is the fraction of relevant results among the retrieved results. It ranges from 0 to 1. The closer it gets to 1, the better. For further details, please visit this link.
Recall	Recall is the percentage of total relevant results correctly classified by the model. In other words, it is the fraction of the total amount of relevant instances that were actually retrieved. It has a range from 0 to 1. The closer it gets to 1, the better. For further details, please visit this link.