Minor updates

Author: GraemeMalcolm

02 - Regression.ipynb

@@ -413,7 +413,7 @@
     "\n",
     "You can quantify the residuals by calculating a number of commonly used evaluation metrics. We'll focus on the following three:\n",
     "\n",
-    "- **Mean Square Error (MSE)**: The mean difference between predicted and actual, squared. This yields a relative metric in which the smaller the value, the better the fit of the model\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",
     "\n",