Optimization Of Data Collection Techniques For Machine Learning

Abstract:

Hello Readers!!! Let’s look at what we are covering in this blog:

• As we all know Machine Learning and Deep Learning requires massive datasets. But the problem is data collection techniques have very limited guidelines.
• When we collect data, generally we are having some target like to predict cost, profit or something like that.
• This Target is the main problem for creating a bias in collected data.
• So, in this we will learn about some mathematical and statistical techniques which will help us to collect data in more efficient and profound way.

Note – Hello Readers!!! This Being Interpretable Blog, Content from original research paper may not match with content in interpretable blog as this is completely made for non-technical people by replacing mathematical equations with simple language.

Introduction:

• Deep Learning models requires large datasets to meet performance targets.
• Over-estimating data needs incurs costs.
• There is huge gap between data collection techniques based on maths and practical data collection techniques.
• We learn in detail about some mathematical data collection techniques and intuition behind them.
  
  

1. Learning Curves and Neural Scaling Laws

A. What are Learning Curves:

• Graphical representation of model performance vs. amount of training data.
• This shows how performance improves as more data or training time is used.
• Helps identify underfitting, overfitting, or when additional data yields diminishing results.

B. Neural Scaling Laws:

• Empirical laws showing how neural network performance scales with increased data, model size and compute.
• Typically, error decreases as there is increase in data size, model parameter, or training steps.
• Key insights = Bigger Models + More Data = better performance.
  
  

2. Active Learning 

Definition: A machine learning paradigm where the model actively queries the most informative data points to label.

• Goal: maximize learning efficiency by labeling fewer but more valuable examples.

Why Active Learning?

• Labeling data can be expensive and time-consuming.
• Active learning reduces labeling cost by focusing on uncertain or ambiguous samples.

Examples:
Uncertainty Sampling - pick data points where the model is least confident.
Query-by-Committee - use disagreement among multiple models to select samples.
Expected Model Change - select samples expected to cause greatest update in the model.

3. Statistical Learning Theory

Overview:

• Provides a theoretical framework for understanding the process of learning from data.
• Formalizes concepts like generalization, overfitting, and model complexity.

Key Concepts:

• VC Dimension: Measures model capacity or complexity (how flexible a model is).
• Bias-Variance Tradeoff: Balance between underfitting (high bias) and overfitting (high variance).
• Generalization Bounds: Theoretical guarantees on how well a model trained on finite data will perform on unseen data.
  
  

4. Optimal Experiment Design 

Definition:

• The process of planning experiments to maximize the information gained while minimizing cost and effort.
• Important in scientific research, clinical trials, and industrial processes.

Types:

• D-Optimal Design: Maximizes determinant of the information matrix, improving parameter estimates.
• A-Optimal Design: Minimizes average variance of estimates.
• Sequential Design: Adaptively chooses experiments based on previous results.

Benefits:

• Efficient resource use.
• Improved accuracy in parameter estimation.
• Faster discovery and decision-making.
  
  

5. Statistical Decision Making

What is it?

• Making a sequence of decisions over time, where each decision affects future outcomes and available information.
• Central to reinforcement learning, control systems, and online learning.

Key Elements:

• States: Current situation or context.
• Actions: Choices available.
• Rewards: Feedback signal to evaluate actions.
• Policy: Strategy for choosing actions based on states.

Applications:

• Robotics, autonomous vehicles, game playing, recommendation systems
  
  

6. Optimal Data Collection 

Definition:

The process of strategically selecting what data to collect, when, and how much, to maximize model performance or decision quality with minimal cost.

Key Ideas:

• Informative Sampling: Prioritize data that reduces uncertainty or improves model accuracy the most.
• Budget-Aware Collection: Work under real-world constraints (e.g., limited time, money, or labeling capacity).
• Adaptive Collection: Dynamically adjust what data to collect based on what’s already known.

Applications:

• Medical diagnostics (collect only crucial tests), sensor placement, survey design, remote sensing

7. Naive Estimation (1)

Definition:

• Simple, baseline estimation methods that ignore model uncertainty, adaptive feedback, or the broader data collection process.

Characteristics:

• Often uses full dataset or assumes i.i.d. (independent and identically distributed) data.
• Ignores feedback loops or adaptivity (i.e., data affects model and vice versa).
• Can lead to biased or inefficient estimations in real-world adaptive systems.

Examples:

• Estimating the mean from collected data without accounting for how the data was gathered. 

• Using standard regression on adaptively collected data (e.g., in a multi-armed bandit setting).
  
  

7. Naive Estimation (2) 

Why It Matters:

• Naive estimators may look optimal under static assumptions but can perform poorly in dynamic, real-world environments.
• Motivates need for robust, adaptive estimation methods.
  
  

8. Learn - Optimize - Collect (Loc) - (1)

Definition

• A feedback-driven loop in decision-making systems that integrates learning from data, optimizing actions, and collecting new data.

The Loop

1. Learn: Estimate unknown parameters or model from collected data.
2. Optimize: Use the learned model to choose the best actions (e.g., policies, decisions).
3. Collect: Take actions and observe new data, closing the feedback loop.

Why It's Important?

• Real-world systems often operate in cycles (e.g., personalization systems, clinical trials, marketing campaigns).
• Decisions affect the future data you observe — breaking the i.i.d. assumption.
• LOC captures the dynamic interaction between estimation and data acquisition.
  
  

8. Learn - Optimize - Collect (Loc) - (2) 

Challenges:

• Bias in learning due to adaptive data collection.
• Need for exploration (collecting uncertain data) vs. exploitation (using current knowledge).

Related To:

• Reinforcement Learning, Bandits, Causal Inference, Experimental Design.
  
  

Some Final Thoughts... 

So Readers, we have seen numerous data optimization techniques.

But as being statistician and coming from hardcore mathematics background, I will write few things:

1. Every technique we have seen on earlier slides are performed practically first and then they are on paper.
2. Secondly, every technique has its due date.
3. Technique selection for collecting data should be done on real-time by seeing nature of our goal.
4. Modifications of data collection techniques can be definitely done to achieve more appropriate results as data science requires practical evidence, theory is always secondary to practical experiences.