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# Regression with Multiple Outputs

The aim here is to predict multiple values, instead of just a single value. This is analogous to multi-label classification where we attempt to predict multiple classses at once.

Reasons to do so:

* less training time
* less number of weights, therefore, less prone to overfitting
* possible relation between values being predicted can be learned

The Error Function is as follows:

$$E = \sum\_{i=1}^k \sum\_{t=1}^N (r\_i^t-y\_i^t)^2$$

i.e. the *mean squared error*. (k is the number of values to be predicted)

* can use stochastic gradient descent
* can use mini-batches in the gradient descent