Machine learning is a complex field of artificial intelligence focused on automating analytical model building. The exponential growth of data volume opens up possibilities for advanced data analysis technique research in machine learning model development. Based on allowing models to learn from data, they require minimal human intervention proving their efficiency over human labor.
While a well-working machine learning model that can complete its task well is always the ultimate goal, the reality is that sometimes data or parameter-related issues prevent it from reaching its full potential. There are numerous problems you can face when training your machine learning model, including the following:
Although it may seem that there is a lot that can go wrong when training a machine learning model, there are as many solutions that help prevent such issues.
Below, we will discuss machine learning model overfitting and how the data needs to be prepared to make sure it is avoided as much as possible.
Model overfitting is a prevalent issue in training computer vision models. Once the machine learning model is trained, an overfitted model can be determined by low error rates and high variance. One of the earliest pre-processing steps is to split the dataset into training and validation samples, which helps us evaluate the model performance.
While it is not possible to know whether the model will overfit beforehand, there are several ways to prepare your dataset to prevent it as much as possible.
Regularization improves the generalization of the algorithm by making minor changes to the dataset. Some examples of the most common ones are listed below.
Knowing what will work best for your specific machine learning model depends on your data and the task it is being trained for. After the model is trained, its performance can be tested on a separate set of data to see how it generalizes to unseen data.
A model that generalizes to unseen data and returns accurate predictions is the ultimate goal in training a machine learning model. In reality, the data, model features, or training time can cause the model to run into some issues. One of the biggest ones – overfitting – occurs when the machine learning model memorizes everything from the training dataset rather than learning from it.
There are several techniques that can be implemented to reduce the risk of overfitting ranging from data augmentation, increased data variety, and the use of a bigger amount of clean data, to choosing the right features and stopping early if there is no more improvement in the validation set. Since every computer vision task is different and highly depends on its data, finding the right techniques for model training may require some time and patience.
There are several ways you can prevent your machine learning model from overfitting when using the SentiSight.ai computer vision platform:
The stopping configuration defines when the model training will be stopped if the performance on the validation set does not improve for the chosen amount of time (in minutes). Stopping the training earlier does not give enough time for the model to start memorizing data instead of learning from it. As a result, preventing the overfitting of the machine learning model.
SentiSight.ai is an online image recognition platform that allows its users to label their dataset, and train object detection, image classification, or instance segmentation models. These models can be deployed to improve various industries such as defect detection in manufacturing or retail just to name two.
To get started with your project, check our blog post library about how to choose the right image labeling tool for the job and how to manage the project when working in a team.
For more information or assistance with your custom projects, contact us directly. SentiSight.ai machine learning platform is here to assist you in completing the project you have always dreamed of.