Training Classifiers with Limited Labeled Data

Requirements Clarification & Assessment

1. Understanding the Dataset:

   • Size: Determine the size of the dataset and the proportion of labeled vs. unlabeled data.
   • Features: Identify the features available and their relevance to the classification task.
   • Class Distribution: Assess the distribution of classes within the labeled data to identify any imbalance.
   • Data Quality: Evaluate the quality of the data for noise and missing values.

2. Objective:

   • Goal: Clearly define the goal of the classification task (e.g., accuracy, precision, recall).
   • Constraints: Identify constraints such as time, computational resources, and labeling costs.

3. Environment:

   • Resources: Assess the available computational resources and tools for implementation.
   • Stakeholders: Identify key stakeholders and their expectations for the project outcome.

4. Exploration:

   • Initial Analysis: Conduct exploratory data analysis (EDA) to understand the data's basic properties and patterns.
   • Preprocessing Needs: Identify preprocessing steps required, such as normalization, encoding, or feature selection.