SVMs and Deep Learning for Classification Tasks

Requirements Clarification & Assessment

1. Understanding the Context:

   • Objective: Determine when to choose SVMs over deep learning models for classification tasks.
   • Scope: Focus on comparing SVMs with deep learning models, while also considering other non-deep learning models like logistic regression.

2. Data Characteristics:

   • Data Volume: Assess the amount of data available. SVMs are preferable when data is limited.
   • Dimensionality: Consider the number of features. SVMs perform well with high-dimensional data.
   • Class Imbalance: Identify if there's a significant class imbalance, which could affect SVM performance.

3. Resource Constraints:

   • Computational Power: Evaluate available computational resources. SVMs require less computational power than deep learning models.
   • Budget: Consider budget constraints for model training and maintenance.

4. Performance Requirements:

   • Accuracy vs. Interpretability: Determine the priority between achieving high accuracy and having an interpretable model.
   • Real-time Processing: Assess if real-time predictions are necessary, influencing model selection.

5. Deployment Environment:

   • Infrastructure: Consider the deployment environment's infrastructure capabilities, such as memory and processing power.