SAGA.imagery_opencv.12: Logistic Regression (OpenCV)

{"id": "SAGA.imagery_opencv.12", "title": "Logistic Regression (OpenCV)", "description": "Integration of the OpenCV Machine Learning library for Logistic Regression based classification of gridded features. <br/><br/>Optimization algorithms like <i>Batch Gradient Descent</i> and <i>Mini-Batch Gradient Descent</i> are supported in Logistic Regression. It is important that we mention the number of iterations these optimization algorithms have to run. The number of iterations can be thought as number of steps taken and learning rate specifies if it is a long step or a short step. This and previous parameter define how fast we arrive at a possible solution. <br/><br/>In order to compensate for overfitting regularization can be performed. (L1 or L2 norm). <br/><br/>Logistic regression implementation provides a choice of two training methods with <i>Batch Gradient Descent</i> or the <i>Mini-Batch Gradient Descent</i>. 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