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Data Interview Question

SVMs and Deep Learning for Classification Tasks

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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.