A loss function that determines the exact difference between label values and model predictions. L1 loss is less outlier-sensitive than L2.
Applications of L1 Loss
Object detection
L1 loss helps calculate the absolute difference between predicted and ground truth bounding boxes.
Anomaly detection
L1 loss is used to detect anomalies or outliers in data by identifying data points with high loss values.
Time series analysis
L1 loss is helpful in measuring the absolute difference between forecasted vs. actual values in time series data.
Linear regression
L1 loss is commonly used as a cost function to limit the difference between predicted and actual values. It is especially useful when the target variable is sparsely represented.
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