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Does squared loss function sensitive to outliers?
The MSE function is very sensitive to outliers because the difference is a square that gives more importance to outliers. MSE is very useful if outliers are important for the problem if outliers are noisy or bad data or bad measures you should use the MAE loss function.
Which technique supports different loss function in machine learning?
Binary Cross Entropy Loss Likewise, a smaller value indicates a more certain distribution. This makes binary cross-entropy suitable as a loss function – you want to minimize its value. We use binary cross-entropy loss for classification models which output a probability p.
Which of the following regression losses is more prone to outliers?
We know that median is more robust to outliers than mean, which consequently makes MAE more robust to outliers than MSE. One big problem in using MAE loss (for neural nets especially) is that its gradient is the same throughout, which means the gradient will be large even for small loss values.
What’s the difference between validation loss and training loss?
If validation loss << training loss you can call it underfitting. Your aim is to make the validation loss as low as possible. Some overfitting is nearly always a good thing. All that matters in the end is: is the validation loss as low as you can get it. This often occurs when the training loss is quite a bit lower.
How is training loss calculated in Keras framework?
During training, frameworks like Keras will output the current training loss to the console. The loss is calculated as a moving average over all processed batches, meaning that in the early training stage when loss drops quickly the first batch of an epoch will have a much higher loss than the last.
When to use quantile loss in machine learning?
Quantile loss is actually just an extension of MAE (when the quantile is 50th percentile, it is MAE). The idea is to choose the quantile value based on whether we want to give more value to positive errors or negative errors.
What to do about validation loss in deep learning?
If validation loss > training loss you can call it some overfitting. If validation loss < training loss you can call it some underfitting. If validation loss << training loss you can call it underfitting. Your aim is to make the validation loss as low as possible. Some overfitting is nearly always a good thing.