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v2 PreviewStart Simple - The Power of Linear Models

Start Simple - The Power of Linear Models

Simple tool for complex problems

Simple tool for complex problems

In the era of complex deep learning architectures, it’s easy to overlook the foundational workhorses of machine learning: linear models1. Yet, adhering to the principle of “start simple” - a form of Occam’s Razor - is often the most effective strategy. Linear models provide a robust, interpretable, and efficient baseline for many prediction tasks.

This tutorial focuses on the “why” and “how” of linear models, specifically linear regression. We’ll cover their importance, the mechanics, essential data preparation steps, and a practical implementation.

Tutorial Goals

  • Understand the value proposition of linear models in modern ML
  • Understand the core concepts behind linear regression
  • Learn essential feature engineering techniques for linear models
  • Implement and evaluate a linear regression model using Scikit-learn
  • Identify scenarios where starting with a linear model is advantageous

Why Linear Models Still Matter

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References

Footnotes

  1. Scikit-learn Linear Models Documentation  ↩

  2. Scikit-learn StandardScaler ↩

  3. Scikit-learn Pipeline ↩

  4. Scikit-learn OneHotEncoder ↩

  5. California Housing Dataset  ↩

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