In this video, learn how to use hyperparameters of a model to achieve proper bias/variance tradeoff.
- [Instructor] Over the last few lessons, … we've been slowly putting all the pieces together. … We'll continue that in this lesson … as we talk about how hyperparameter tuning … can help you achieve the right bias-variance tradeoff. … So we've defined underfit and overfit, … and now we understand how to identify it … using the insights from this plot. … But what we haven't really talked about is … what actually causes it to happen and how we can fix it. … That's where hyperparameter tuning comes into play. … So there are actually two methods … to tune a model for optimal complexity. … The first is hyperparameter tuning. … That's choosing a set of optimal hyperparameters … for fitting an algorithm. … So this is what we'll cover in this section, … including defining what a hyperparameter actually is. … The second is regularization. … This is a technique used specifically to reduce overfitting … by discouraging overly complex models, … and we'll cover this in the next lesson. … Okay, so let's start with some definitions, …
Author
Released
5/10/2019- What is machine learning (ML)?
- ML vs. deep learning vs. AI
- Handling common challenges in ML
- Plotting continuous features
- Continuous and categorical data cleaning
- Measuring success
- Overfitting and underfitting
- Tuning hyperparameters
- Evaluating a model
Skill Level Beginner
Duration
Views
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Introduction
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What you should know1m 6s
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Using the exercise files1m 24s
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1. Machine Learning Basics
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Why Python?5m 49s
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Common challenges6m 4s
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2. Exploratory Data Analysis and Data Cleaning
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Plotting continuous features7m 35s
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Continuous data cleaning5m 44s
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Categorical data cleaning4m 33s
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3. Measuring Success
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Why do we split up our data?5m 54s
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4. Optimizing a Model
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What is underfitting?2m 26s
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What is overfitting?2m 47s
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Finding the optimal tradeoff3m 16s
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Hyperparameter tuning6m 22s
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Regularization2m 31s
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5. End-to-End Pipeline
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Overview of the process1m 48s
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Clean categorical features4m 18s
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Tune hyperparameters6m 34s
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Conclusion
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Next steps1m 23s
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Video: Hyperparameter tuning