Statistical Analysis and Modeling in R: Building Regularized Models & Ensemble Models
R Programming 4.0+
| Expert
- 14 Videos | 1h 31m 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
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discover the key concepts covered in this courserecall characteristics of overfitted and underfitted modelsdescribe the bias-variance trade-offexamine and interpret the data for regressionperform ordinary least squares (OSL) regressionprepare data to build regularized regression modelsperform and evaluate Ridge regression
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perform and evaluate Lasso regressionperform and evaluate ElasticNet regressionoutline the main characteristics of ensemble learningexamine and visualize data for regressionperform regression using decision treesperform regression using random forestsummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview2m 7sUP NEXT
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2.Overfitting and Underfitting Machine Learning Models9m 51s
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3.The Bias-Variance Trade-off6m 18s
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4.Exploring and Understanding Data for Regression9m 21s
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5.Performing Ordinary Least Squares (OLS) Regression5m 41s
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6.Preparing Data for Regularized Regression Models4m 31s
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7.Performing Ridge Regression in R10m 42s
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8.Performing Lasso Regression in R9m 19s
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9.Performing ElasticNet Regression in R6m 43s
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10.Recognizing Ensemble Learning7m 36s
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11.Using R to Explore and Visualize Data4m 54s
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12.Performing Regression Using Decision Trees in R5m 45s
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13.Performing Regression Using Random Forest in R6m 2s
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14.Course Summary2m 22s
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