Update README.md

README.md

@@ -21,7 +21,7 @@ in this case it is used to track the relative density of lanternfly sightings in
 This model is a KDE. This is an unsupervised model that 
 estimates the density of continuous values from discrete points. 
 
-This model is from the SciPy library and stored to allow for rapid access. 
+This is an off the shelf model from the SciPy library and stored to allow for rapid access. 
 
 - **Developed by:** Devin DeCosmo
 - **Model type:** Image Classifier
@@ -44,66 +44,85 @@ The direct use is classifying our lanternfly sighting samples from our geolocal
 As the Gaussian KDE is a generalized unsupervised learning model, this could be used
 for other datsets with latitude/longitude coordinates. 
 
+
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
+KDE's are unable to perform regression or classification on out of set data. 
+They can only predict concentration within the space of the provided data. 
 
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
+This KDE can only use the data in our current dataset. At this time
+that is data at CMU during Fall 2025. This puts geographic and temporal 
+contstraints on the current model fit. 
+
+This model only shows the highest concentration of lanternflies. It does 
+not and can not make any estimations of reasons for these density measurments.
+Additional tools are needed to use the KDE outputs in useful research tasks. 
 
 
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
+This model is recommended to be used with data gathered with a specific area and time period in mind. 
+This will allow the KDE to accurately model the data and regions provided. 
 
 
+## Training and Testing Details
 
-## Training Details
-
-### Training Data
+### Training and Testing Data
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-rlogh/lanternfly_swatter_training
+This model was trained on our geolocal dataset rlogh/lanternfly_swatter_training
 
-### Training Procedure
+### Training and Testing Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+KDE models do not train like standard ML models. Instead they read the 
+entire dataset, or subset of data, and calculate the relative densities based on
+the proximity of points. 
 
+#### Training and Testing Hyperparameters
 
-#### Training Hyperparameters
-
+The smoothing and calculations of the KDE can be altered depending on the 
+bandwidth estimation method used. 
 
+In this case, the standard value of "scott" was used. This allowed for 
+a middle ground between distinct small clusters and larger overall trends.
+Additional experimentation with the bandwidth method could be necessary 
+for future datasets with different.
 
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
+There are no metrics like accuracy for unsupevised models. To ensure the 
+data fits the dataset correctly the plot is inspected by hand. This included
+testing different bandwith parameters like Scott, silverman, and integer
+values to determine the best fit. From this, the scott was determined to 
+show the most easily readable values for hotspot. 
 
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
+### Results
 
+From this, we have a useful, lightweight model from SciPy that can 
+rapidly model the relative densities of collected lanternfly data. 
 
-### Results
+The limits of these result from the bandwidth parameters of and limits of the KDE
+function. In future if the bandwidth could be adjusted automatically based on the 
+input region the models could be made more generalizable. 
 
 
 #### Summary
 
+This model is a pre-built KDE from the SciPy library. In this case,
+it is being used to map different lanternfly datapoints for research
+and user purposes. 
+