*L1 or Lasso Regularization*
L1 or Lasso Regularization is a type of regularization that helps to reduce the effect of outliers on the training set.
L1 regularization is a simple way to regularize your model, but it can overfit to your data and make predictions that are too accurate. If you have lots of data points and your models are performing well, you might want to consider using a more advanced method like L2 or L3 regularization.
L1 regularization is an algorithm for finding the parameters of a function that minimizes the Frobenius norm of its gradient. It does this by minimizing the sum of squared differences between the true function and the function that has been minimized. In other words, it tries to find a set of parameters such that the loss function is as small as possible. The L1 error cost is easy to compute: it is simply the sum of squared errors on all training samples.
In practice, we can use L1 regularization to avoid overfitting by allowing us to learn only a small part of our model. This makes it possible to calculate how much weight each training sample should have in determining how much influence it will have on future predictions.
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