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--- |
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'[object Object]': null |
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license: mit |
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datasets: |
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- ddecosmo/lanternfly_training_dataset |
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language: |
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- en |
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--- |
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# Model Card for {{ model_id | default("Model ID", true) }} |
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<!-- Provide a quick summary of what the model is/does. --> |
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This is an off the shelf KDE model from SciPy. It is Kernel Density Estimator, |
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in this case it is used to track the relative density of lanternfly sightings in Pittsburgh. |
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## Model Details |
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### Model Description |
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This model is a KDE. This is an unsupervised model that |
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estimates the density of continuous values from discrete points. |
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This is an off the shelf model from the SciPy library and stored to allow for rapid access. |
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- **Developed by:** Devin DeCosmo |
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- **Model type:** Image Classifier |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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- **Finetuned from model:** SciPy Gaussian KDE |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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This model is used to estimate the density of values in proportion to each other. |
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From 0 - 1. In this case, it uses longitude and latitude as X,Y coordinates to perform this analysis. |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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The direct use is classifying our lanternfly sighting samples from our geolocal dataset. |
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As the Gaussian KDE is a generalized unsupervised learning model, this could be used |
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for other datsets with latitude/longitude coordinates. |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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KDE's are unable to perform regression or classification on out of set data. |
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They can only predict concentration within the space of the provided data. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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This KDE can only use the data in our current dataset. At this time |
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that is data at CMU during Fall 2025. This puts geographic and temporal |
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contstraints on the current model fit. |
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This model only shows the highest concentration of lanternflies. It does |
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not and can not make any estimations of reasons for these density measurments. |
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Additional tools are needed to use the KDE outputs in useful research tasks. |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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This model is recommended to be used with data gathered with a specific area and time period in mind. |
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This will allow the KDE to accurately model the data and regions provided. |
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## Training and Testing Details |
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### Training and Testing Data |
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<!-- 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. --> |
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This model was trained on our geolocal dataset rlogh/lanternfly_swatter_training |
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### Training and Testing Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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KDE models do not train like standard ML models. Instead they read the |
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entire dataset, or subset of data, and calculate the relative densities based on |
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the proximity of points. |
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#### Training and Testing Hyperparameters |
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The smoothing and calculations of the KDE can be altered depending on the |
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bandwidth estimation method used. |
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In this case, the standard value of "scott" was used. This allowed for |
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a middle ground between distinct small clusters and larger overall trends. |
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Additional experimentation with the bandwidth method could be necessary |
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for future datasets with different. |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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There are no metrics like accuracy for unsupevised models. To ensure the |
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data fits the dataset correctly the plot is inspected by hand. This included |
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testing different bandwith parameters like Scott, silverman, and integer |
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values to determine the best fit. From this, the scott was determined to |
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show the most easily readable values for hotspot. |
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### Results |
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From this, we have a useful, lightweight model from SciPy that can |
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rapidly model the relative densities of collected lanternfly data. |
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The limits of these result from the bandwidth parameters of and limits of the KDE |
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function. In future if the bandwidth could be adjusted automatically based on the |
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input region the models could be made more generalizable. |
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#### Summary |
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This model is a pre-built KDE from the SciPy library. In this case, |
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it is being used to map different lanternfly datapoints for research |
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and user purposes. |
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