Cloudwalk has made a number of significant functional breakthroughs in this field:
The model can automatically search and mine features. With the search strategy manually configured, the model is able to automatically process and screen features to find the most effective feature combination, and apply the automatic feature engineering to mine feature combinations and bin the features, without manually selecting the features and adjusting the binning. Users only need to define the search range of model parameters, and the algorithm can automatically search and adjust the optimal model parameters. Using the decision-making tree for feature extraction, this model is characterized by strong nonlinear feature fitting ability. Experiments on multiple datasets show that with an effect similar to that of other models established based on the decision-making tree algorithm, e.g. LGB and XGB, this model can output the risk value corresponding to each feature and feature combination and then deploy these values through the decision-making engine.
Background and challenges
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The actual business, operation, and regulatory aspects of the one card system no longer fully meet the park's needs for digital and intelligent updates and changes, such as the production, maintenance, and updating of physical cards, which increases management costs -
The lack of effective reporting and handling methods for card recognition without identifying individuals can easily lead to theft and customer complaints -
The online and offline business is disconnected and can only be applied to physical stores, unable to expand online business and management
