Update 02. Estimated area and uncertainty in Machine Learning.ipynb

examples/notebooks/02. Estimated area and uncertainty in Machine Learning.ipynb

@@ -26,7 +26,9 @@
    "source": [
     "In this example, after obtaining the predicted class map, we are in a case where we want to know the uncertainties of each class. The assessing accuracy and area estimate will be obtained following guidance proposed by [(Olofsson et al., 2014)](https://doi.org/10.1016/j.rse.2014.02.015). All that users need are the confusion matrix and a previously obtained predicted class map.\n",
     "\n",
-    "> **Keep in mind**: the most critical recommendation is that the sampling design should be a *probability sampling design*. An essential element of probability sampling is that randomization is incorporated into the sample selection protocol. Various probability sampling designs can be applied for precision assessment and area estimation, the most commonly used designs being simple random, stratified random, and systematic. Please see the workd by [Olofsson et al. (2014)](https://doi.org/10.1016/j.rse.2014.02.015) for more details. "
+    "> **Keep in mind**: the most critical recommendation is that the sampling design should be a *probability sampling design*. An essential element of probability sampling is that randomization is incorporated into the sample selection protocol. Various probability sampling designs can be applied for precision assessment and area estimation, the most commonly used designs being simple random, stratified random, and systematic. Please see the workd by [Olofsson et al. (2014)](https://doi.org/10.1016/j.rse.2014.02.015) for more details.\n",
+    "\n",
+    "For this particular example, the samples follow a simple random sample design."
    ]
   },
   {