New Problem: ROC AUC #324
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New Problem: ROC AUC #324
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Implementation of ROC Area Under Curve, a metric used for measuring predictive quality of a binary classifier. Input: true labels and predicted probabilities Output: ROC AUC rounded to 5 floating points
The logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables. * Input: * X, y * learning_rate * iterations - n of epochs * Output: * list of updated parameters * list of recorded losses over iterations (starting from 0)
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Solves #235 |
Closed
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need to update how we render the learn section on the site, so it deals with charts better in the learn section, but the code and the learn section seem good |
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Implementation of ROC Area Under Curve, a metric used for measuring predictive quality of a binary classifier.
Input: true labels and predicted probabilities
Output: ROC AUC rounded to 5 floating points