# Iterative Training Model training is an iterative process. As you begin testing out real world examples on your model, you can identify weaknesses and use test results to pinpoint areas for improvement. The first thing to adjust to improve your model output is the **Confidence Threshold**. If your model didn’t find an object you wanted it to, see if it picks it up with a lower confidence threshold. If your model is predicting too many of your object and you’re getting a lot of false positives, see if you can increase the confidence threshold to get better results. If you’re not able to balance the confidence for these two scenarios, begin to analyze the image you’re working with. First, use the button to send this image to your overall dataset so that you can train your model on this specific image the next time around. Then, go out and find similar images to add into your dataset. Think about lighting conditions, camera angle, object size, background noise, etc. Think through what might be confusing for the model and bring in more similar examples. Repeat this process to enhance accuracy and reliability until you are satisfied with your model results. Congrats! You’ve done it. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/iterative-training.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.