Machine learning downsampling

1. What is downsampling in machine learning?
2. Why downsample machine learning?
3. Does downsampling reduce accuracy?
4. Why do we use downsampling?

What is downsampling in machine learning?

Downsampling (in this context) means training on a disproportionately low subset of the majority class examples. Upweighting means adding an example weight to the downsampled class equal to the factor by which you downsampled.

Why downsample machine learning?

Downsampling is a mechanism that reduces the count of training samples falling under the majority class. As it helps to even up the counts of target categories. By removing the collected data, we tend to lose so much valuable information.

Does downsampling reduce accuracy?

With increasing downsampling rate, all models' precision increases, since more background information is provided within the training set to avoid false positives. In contrast to the two-stage detectors, SSD has much lower detection accuracy.

Why do we use downsampling?

Downsampling enables you to create even smaller models since the machine learning algorithm doesn't require as many training data points. For embedded AI, memory usage is vital; creating a smaller but still highly accurate model allows you to save space for other application code and processes on the device.