3. Formulating Statistical Hypotheses for Machine Learning

AI Depth School · Beginner ·📐 ML Fundamentals ·4mo ago

About this lesson

In this video, we cover the exact structure of Hypothesis Testing applied to Machine Learning. We explain the adversarial relationship between the Null Hypothesis (H0) and the Alternative Hypothesis (H1), and why the burden of proof is always on the new model. We also differentiate between One-Sided vs Two-Sided tests in the context of product experiments and introduce the critical concept of Pre-registration to prevent p-hacking. Key Concepts: - Null Hypothesis (H0) as the Default Stance - Alternative Hypothesis (H1) and Burden of Proof - One-Sided vs Two-Sided Tests - Pre-registration & Preventing P-Hacking

Original Description

In this video, we cover the exact structure of Hypothesis Testing applied to Machine Learning. We explain the adversarial relationship between the Null Hypothesis (H0) and the Alternative Hypothesis (H1), and why the burden of proof is always on the new model. We also differentiate between One-Sided vs Two-Sided tests in the context of product experiments and introduce the critical concept of Pre-registration to prevent p-hacking. Key Concepts: - Null Hypothesis (H0) as the Default Stance - Alternative Hypothesis (H1) and Burden of Proof - One-Sided vs Two-Sided Tests - Pre-registration & Preventing P-Hacking
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