Assign Categories to Fresh Data

Requirements Clarification & Assessment

Before selecting an algorithm to classify fresh data based on a training dataset, it's essential to clarify and assess the specific requirements and characteristics of the problem:

1. Nature of the Problem:

   • Binary vs. Multiclass Classification: Determine if the task involves distinguishing between two classes or multiple categories.
   • Supervised vs. Unsupervised Learning: Identify whether the data includes labels for training.

2. Dataset Characteristics:

   • Size of the Dataset: Consider the volume of data available for training and testing.
   • Type of Data: Assess whether the data is structured, unstructured, or involves text, images, or numerical values.
   • Data Quality: Evaluate the completeness, accuracy, and consistency of the dataset.

3. Objective and Constraints:

   • Accuracy vs. Interpretability: Balance the need for high accuracy with the requirement for model interpretability.
   • Resource Availability: Consider the computational resources and time available for training and deploying the model.
   • Scalability and Real-time Requirements: Determine if the solution needs to scale efficiently or operate in real-time environments.