Statistical Analysis and Modeling in R: Building Regularized Models & Ensemble Models

R Programming 4.0+    |    Expert
  • 14 Videos | 1h 37m 11s
  • Includes Assessment
  • Earns a Badge
Understanding the bias-variance trade-off allows data scientists to build generalizable models that perform well on test data. Machine learning models are considered a good fit if they can extract general patterns or dominant trends in the training data and use these to make predictions on unseen instances. Use this course to discover what it means for your model to be a good fit for the training data. Identify underfit and overfit models and what the bias-variance trade-off represents in machine learning. Mitigate overfitting on training data using regularized regression models, train and evaluate models built using ridge regression, lasso regression, and ElasticNet regression, and implement ensemble learning using the random forest model. When you're done with this course, you'll have the skills and knowledge to train models that learn general patterns using regularized models and ensemble learning.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recall characteristics of overfitted and underfitted models
    describe the bias-variance trade-off
    examine and interpret the data for regression
    perform ordinary least squares (OSL) regression
    prepare data to build regularized regression models
    perform and evaluate Ridge regression
  • perform and evaluate Lasso regression
    perform and evaluate ElasticNet regression
    outline the main characteristics of ensemble learning
    examine and visualize data for regression
    perform regression using decision trees
    perform regression using random forest
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 7s
    UP NEXT
  • Playable
    2. 
    Overfitting and Underfitting Machine Learning Models
    9m 51s
  • Locked
    3. 
    The Bias-Variance Trade-off
    6m 18s
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    4. 
    Exploring and Understanding Data for Regression
    9m 21s
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    5. 
    Performing Ordinary Least Squares (OLS) Regression
    5m 41s
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    6. 
    Preparing Data for Regularized Regression Models
    4m 31s
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    7. 
    Performing Ridge Regression in R
    10m 42s
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    8. 
    Performing Lasso Regression in R
    9m 19s
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    9. 
    Performing ElasticNet Regression in R
    6m 43s
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    10. 
    Recognizing Ensemble Learning
    7m 36s
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    11. 
    Using R to Explore and Visualize Data
    4m 54s
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    12. 
    Performing Regression Using Decision Trees in R
    5m 45s
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    13. 
    Performing Regression Using Random Forest in R
    6m 2s
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    14. 
    Course Summary
    2m 22s

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