In statistics and machine learning, attribute selection is the process of proposing relevant feature subsets (variables, predictors) for model construction. Feature selection techniques are widely used for three reasons: simplifying the model to make it easier for users/researchers to explain, shorter training time, and enhancing generalization ability by reducing overfitting. The central premise of using attribute selection technology is that the data contains many irrelevant or redundant features, so it can be deleted without causing too much information loss. Irrelevant or redundant features are two different concepts, because one related feature may be redundant, while another related feature is largely related to it. Now, CD ComputaBio offers attribute selection service to meet the specific needs of different customers.
The choice of evaluation metric heavily influences the algorithm, and it is these evaluation metrics that distinguish between the three main sections of feature selection algorithms:
|Project name||Attribute selection service|
|Our services||CD ComputaBio offers attribute selection service to meet the specific needs of different customers.|
|Timeline||Decide according to your needs.|
|Deliverable||We provide you with raw data and analysis service.|
CD ComputaBio provides professional attribute selection services to meet the specific needs of regular customers on time and according to budget. Our attribute selection service can be used in fields such as SNPs study, microarray, spectral mass and disease research. CD ComputaBio relies on world-class technical expertise, we provide customers with the best quality one-stop attribute selection service, including the development of experimental procedures according to different experimental needs. Please feel free to contact us for more detailed information, our scientists will tailor the most reasonable plan for your project. If you want to know more service prices or technical details, please feel free to contact us.