Training Models with Bootstrap Resampling: A Powerful Approach to Improving Model Performance
Data scientists have been searching for ways to build models that perform better than traditional approaches. One important category of resampling techniques has emerged as a powerful tool in this quest, and one such technique is called bootstrap resampling. In this article, we will delve into the world of bootstrap resampling and explore its applications in building and evaluating machine learning models.
Bootstrap Resampling: A Technique for Building Better Models
Bootstrap resampling involves drawing with replacement from our original dataset to create new, smaller samples. These samples are then used to fit a model, which is repeated multiple times. The idea behind this technique is that by using these smaller samples, we can reduce the impact of overfitting and improve the generalizability of our models.
Let's consider an example using cars as our training dataset, with 900 cars in total. To create a bootstrap resample, we would draw with replacement 900 times from this dataset to get a new set of 900 cars. Since we are drawing with replacement, it is likely that some cars will be drawn more than once. We then fit our model on this new set of 900 cars, which contains duplicates. This process is repeated multiple times, and after each iteration, all the models fitted on the bootstrap samples are combined, and their average is taken.
The Benefits of Bootstrap Resampling
While traditional approaches to building models can be time-consuming, bootstrap resampling offers a significant advantage in terms of efficiency. As we have seen, training a model with this technique requires specifying `method equals boot` in `train control`, which makes it easy to incorporate into our workflow. The default behavior is to perform 25 bootstrap resampling iterations, but this can be adjusted as needed.
Evaluating Models with Bootstrap Resampling
When evaluating models built using bootstrap resampling, we use metrics from the Yardstick package. Each car has a real fuel efficiency rating reported by the Department of Energy, and our models predict fuel efficiency for each car. When evaluating a model, we calculate how far apart each predicted value is from its corresponding real value. This allows us to visualize the differences between our models and get a better understanding of which one performs better.
Visualizing Model Performance
To illustrate the performance of different models, such as linear regression and random forests, we can create plots that show the actual fuel efficiency on the x-axis and the predicted fuel efficiency on the y-axis. By visualizing these differences, we can gain insight into which model is performing better in this specific dataset.
Now it's Your Turn: Fitting Models with Bootstrap Resampling
In this lesson, you will have the opportunity to fit models using bootstrap resampling and compare their performance. You will use a subset of the complete data set for this exercise, as bootstrapping requires multiple iterations. With the help of `train control`, specifying `method equals boot` is easy, allowing you to incorporate this powerful technique into your workflow.
The Importance of Bootstrapping
Bootstrapping offers several benefits over traditional approaches to building models. By using bootstrap resampling, we can reduce the impact of overfitting and improve the generalizability of our models. Additionally, bootstrapping allows us to easily evaluate the performance of different models and visualize their strengths and weaknesses.
As you work with bootstrap resampling in this lesson, keep in mind that it may take longer than training a model once. However, the payoff is well worth the extra time and effort. With its ease of use and powerful benefits, bootstrapping has become an essential tool for data scientists looking to improve their models and achieve better performance.
"WEBVTTKind: captionsLanguage: enyou just built and then evaluated models that were trained one time on the whole training set at once data scientists have come up with a slew of approaches to build models that perform better than this approach and a lot of important ones fall under the category of resampling the first resampling approach we're going to try in this course is called the bootstrap bootstrap resampling means drawing with replacement from our original data set and then fitting on that data set let's think about cars let's say our training dataset has 900 cars in it to make a bootstrap resample we draw with replacement 900 times from that training set to get the same sized sample that we started with since we're drawing with replacement we will probably draw some cars more than once we then fit our model on that new set of 900 cars that contains some duplicates then we do that again we draw 900 times from the training set with replacement and fit a model we repeat that some number of times look at all the models we fit on the bootstrap samples combine them and then take an average of some kind this approach does take longer obviously than training the data one time in your exercise you will have a subset of the complete data set to try this out with I am very happy to be able to tell you that training a model with bootstrap resampling is pretty darn easy and carrot all you have to do is specify method equals boot in train control like you see here the default behavior is to do 25 bootstrap resampling x' but you can change this if you want to we are going to evaluate our models again using metrics from the yardstick pack and I want to emphasize what it is that we are comparing when we do that each car has a real fuel efficiency as reported by the Department of Energy and then we have built models that predict fuel efficiency for each car when we evaluate a model we are calculating how far apart each predicted value is from each real value in this lesson you also are going to visualize these differences like you see here the x-axis has the actual fuel efficiency and the y-axis has the predicted fuel efficiency for each kind of model the difference between linear regression and random forests isn't huge here but in this case we can see visually that the random forest model is performing better okay now it's your turn let's see if you can fit these kinds of models with bootstrap resampling and find which model performs betteryou just built and then evaluated models that were trained one time on the whole training set at once data scientists have come up with a slew of approaches to build models that perform better than this approach and a lot of important ones fall under the category of resampling the first resampling approach we're going to try in this course is called the bootstrap bootstrap resampling means drawing with replacement from our original data set and then fitting on that data set let's think about cars let's say our training dataset has 900 cars in it to make a bootstrap resample we draw with replacement 900 times from that training set to get the same sized sample that we started with since we're drawing with replacement we will probably draw some cars more than once we then fit our model on that new set of 900 cars that contains some duplicates then we do that again we draw 900 times from the training set with replacement and fit a model we repeat that some number of times look at all the models we fit on the bootstrap samples combine them and then take an average of some kind this approach does take longer obviously than training the data one time in your exercise you will have a subset of the complete data set to try this out with I am very happy to be able to tell you that training a model with bootstrap resampling is pretty darn easy and carrot all you have to do is specify method equals boot in train control like you see here the default behavior is to do 25 bootstrap resampling x' but you can change this if you want to we are going to evaluate our models again using metrics from the yardstick pack and I want to emphasize what it is that we are comparing when we do that each car has a real fuel efficiency as reported by the Department of Energy and then we have built models that predict fuel efficiency for each car when we evaluate a model we are calculating how far apart each predicted value is from each real value in this lesson you also are going to visualize these differences like you see here the x-axis has the actual fuel efficiency and the y-axis has the predicted fuel efficiency for each kind of model the difference between linear regression and random forests isn't huge here but in this case we can see visually that the random forest model is performing better okay now it's your turn let's see if you can fit these kinds of models with bootstrap resampling and find which model performs better\n"
