Merge pull request MicrosoftDocs#31 from callezenwaka/patch-2

Update 02 - Regression.ipynb

02 - Regression.ipynb

@@ -417,7 +417,7 @@
     "\n",
     "- **Mean Square Error (MSE)**: The mean of the squared differences between predicted and actual values. This yields a relative metric in which the smaller the value, the better the fit of the model\n",
     "- **Root Mean Square Error (RMSE)**: The square root of the MSE. This yields an absolute metric in the same unit as the label (in this case, numbers of rentals). The smaller the value, the better the model (in a simplistic sense, it represents the average number of rentals by which the predictions are wrong!)\n",
-    "- **Coefficient of Determination (usually known as *R-squared* or R<sup>2</sup>**: A relative metric in which the higher the value, the better the fit of the model. In essence, this metric represents how much of the variance between predicted and actual label values the model is able to explain.\n",
+    "- **Coefficient of Determination (usually known as *R-squared* or R<sup>2</sup>)**: A relative metric in which the higher the value, the better the fit of the model. In essence, this metric represents how much of the variance between predicted and actual label values the model is able to explain.\n",
     "\n",
     "> **Note**: You can find out more about these and other metrics for evaluating regression models in the [Scikit-Learn documentation](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)\n",
     "\n",