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Minor typo fix in regularization documentation #4012

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Jul 24, 2019
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Minor typo fix in regularization documentation #4012

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4 changes: 2 additions & 2 deletions docs/api-reference/regularization-l1-l2.md
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@@ -1,6 +1,6 @@
This class uses [empricial risk minimization](https://en.wikipedia.org/wiki/Empirical_risk_minimization) (i.e., ERM)
This class uses [empirical risk minimization](https://en.wikipedia.org/wiki/Empirical_risk_minimization) (i.e., ERM)
to formulate the optimization problem built upon collected data.
Note that empricial risk is usually measured by applying a loss function on the model's predictions on collected data points.
Note that empirical risk is usually measured by applying a loss function on the model's predictions on collected data points.
If the training data does not contain enough data points
(for example, to train a linear model in $n$-dimensional space, we need at least $n$ data points),
[overfitting](https://en.wikipedia.org/wiki/Overfitting) may happen so that
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