classification of human activity using tabPFN sample notebook #2189
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- Here you have written TabFPN, will it be TabPFN ?
- Accessing the datasets? but the heading below is Accessing the dataset
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Add required citation/license details as suggested by Rohit. |
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@BP-Ent , could you please review this notebook |
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It enhances workplace safety by identifying risky workers' activities in hazardous industrial environments, such as in mining and on oil rigs, ensuring safety and reducing accidents. Furthermore, in the case of smart cities and urban mobility, HAR data from pedestrians and commuters can be efficiently classified to optimize traffic flow, public transport systems, and urban planning initiatives. Additionally, HAR supports emergency response efforts during disasters by locating people in need of help. TabPFN's speed, simplicity, and effectiveness make it an ideal choice for these real-time HAR applications.
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In the training dataframe above we use Activity as the target label to be predicted, using the rest of the features as explanatory variables X. We define the explanatory variables as follows:
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Once the explanatory variables X are defined, they are used as input in the prepare_tabulardata method from the tabular learner in arcgis.learn. The method takes the feature layer or a spatial dataframe containing the dataset and prepares it for fitting the model.
The input parameters required for the tool are used as follows:
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To get a sense of what the training data looks like, the show_batch() method will randomly pick a few training samples and visualize them. The samples show the explanatory variables and the Activity target label to predict.
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Next, we will evaluate the model's performance. This will print out multiple model metrics that we can use to assess the model quality. These metrics include a combination of multiple evaluation criteria, such as accuracy, precision, recall and F1-Score, which collectively measure the model's performance on the validation set.
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This project highlights the powerful capabilities of the TabPFN classifier for Human Activity Recognition (HAR) tasks. Even with a training dataset of just 1,020 samples, the model achieved impressive results on a larger test dataset of 6,332 samples, with an accuracy of 96.81%, and precision, recall, and F1 scores all reaching 0.97. The TabPFN model's speed, simplicity, and strong performance in classifying human activities, highlight its potential for applications in healthcare, fitness, smart cities and disaster relief operations, offering an efficient and scalable solution for HAR systems.
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@BP-Ent , I have added the suggestions, please check. |
classification of human activity using tabPFN sample notebook