Fixed typo (#145)
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@ -38,7 +38,7 @@ There are two main resampling techniques that we're going to cover.
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the data to the training set, and evaluate it on the test set. This avoids
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the data to the training set, and evaluate it on the test set. This avoids
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intrinsic bias of using the same data to both fit the model and assess it's
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intrinsic bias of using the same data to both fit the model and assess it's
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quality. However it introduces a new bias: you're not using all the data to
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quality. However it introduces a new bias: you're not using all the data to
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fit the model so it's going to be quite as good as it could be.
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fit the model so it's not going to be quite as good as it could be.
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* We will use __boostrapping__ to understand how stable (or how variable)
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* We will use __boostrapping__ to understand how stable (or how variable)
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the model is. If you sample data from the same population multiple times,
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the model is. If you sample data from the same population multiple times,
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