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You might be wondering how the trees in the random forest get created. After all, right now, our algorithm for creating a decision tree is deterministic — given a training set, the same tree will be made every time. To make a random forest, we use a technique called bagging, which is short for bootstrap aggregating. This exercise will explain bootstrapping, which is a type of sampling method done with replacement.

How it works is as follows: every time a decision tree is made, it is created using a different subset of the points in the training set. For example, if our training set had 1000 rows in it, we could make a decision tree by picking 100 of those rows at random to build the tree. This way, every tree is different, but all trees will still be created from a portion of the training data.

In bootstrapping, we’re doing this process with replacement. Picture putting all 100 rows in a bag and reaching in and grabbing one row at random. After writing down what row we picked, we put that row back in our bag. This means that when we’re picking our 100 random rows, we could pick the same row more than once. In fact, it’s very unlikely, but all 100 randomly picked rows could all be the same row! Because we’re picking these rows with replacement, there’s no need to shrink our bagged training set from 1000 rows to 100. We can pick 1000 rows at random, and because we can get the same row more than once, we’ll still end up with a unique data set.

We’ve loaded a dataset about cars here. An important field within the dataset is the safety rating denoted by safety that can be “low”, “med”, or “high.” We’re going to implement bootstrapping and estimate the average safety rating across the different bootstrapped samples.

Instructions

1.

We’ve written some code to print the number of rows in the data set and the distribution of safety ratings of the entire dataset. Uncomment the relevant lines and press Run to view the results of this.

2.

We see that the safety ratings are equally distributed between the three possible values - high, med and low. We’re now going to create a bootstrapped sample using .sample(). .sample() takes two arguments:

  • number of rows: which is the same size as the dataset
  • replace: set to True because bootstrapping is sampling with replacement.

Uncomment the line relevant to it and print the distribution of safety ratings of the new sampled dataset.

3.

Note that the distribution has now shifted! Using the same process, write a for loop to create 1000 bootstrapped samples of the same size as the original dataset. Save the percentage of “low” ratings into an array called low_perc.

4.

We’ve written some code to plot a histogram of the low percentage values. Uncomment the relevant lines and press Run. We see that the average value of the low safety proportion of vehicles spans a range centered around roughly the true mean.

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