The phrase "a classification model for" is correct and usable in written English.
You can use it when discussing a specific type of model used in data science or machine learning that categorizes data into different classes or groups.
Example: "We developed a classification model for predicting customer churn based on their usage patterns."
Alternatives: "a categorization model for" or "a sorting model for".
Cerius2 descriptors were employed to develop a classification model for the 2772 compounds with antipsychotic activity.
Molconn-Z and BCUT topological descriptors were employed to develop a classification model for 1328 dopamine antagonists from MDDR database.
This study used two types of models, a classification model for the RB dataset and continuous models for the other 12 datasets.
A central problem in constructing the efficient classification system is identifying a representative set of features from which to construct a classification model for a particular task.
Consequently, the RF method is more suitable for building a classification model for this dataset and the 2D descriptors can characterize molecules of this dataset more adequately.
According to the set of acoustic features selected in Section 4.2.1, we can compute a classification model for each pair of spectral/temporal features.
Thirdly, a set of features are combined into a supervised scoring model and a classification model respectively for predicting user's check-in location at a fine-grained time in the future.
Results: We present a classification model, which for a specific gene of interest can predict the mapping to a KEGG pathway, based on its domain signature.
More than 2,000 patients' voice recordings were analyzed, and a classification model was proposed for the three SC types.
This paper proposes a novel classification model for identifying frequently observed paths within a specified network structure that can be used to classify known response classes.
First, for given separator characteristics, plausible alternatives of identical stages meeting the product specifications are recognised from a space of design variables (separator characteristics) evaluated from a simple classification model for binary separation.
