# Machine Learning with `StatLearn`

The `StatLearn`

(short for statistical learning) OnlineStat uses stochastic approximation methods to estimate models that take the form:

$\hat\beta = \argmin_\beta \frac{1}{n} \sum_i f(y_i, x_i'\beta) + \sum_j \lambda_j g(\beta_j),$

where

- $f$ is a
**loss function**of a response variable and linear preditor. - $\lambda_j$'s are nonnegative regularization parameters.
- $g$ is a
**penalty function**.

For example, LASSO Regression fits this form with:

- $f(y_i, x_i'\beta) = \frac{1}{2}(y_i - x_i'\beta) ^ 2$
- $g(\beta_j) = |\beta_j|$

OnlineStats implements interchangeable loss and penalty functions to use for both regression and classification problems. See the `StatLearn`

docstring for details.

### Stochastic Approximation

An important note is that `StatLearn`

is unable to estimate coefficients exactly (For precision in regression problems, see `LinReg`

). The upside is that it makes estimating certain models *possible* in an online fashion.

For example, it is **not possible** to get the same coefficients for logistic regression from an O(1)-memory *online* algorithm as you would get from its *offline* counterpart. This is because the logistic regression likelihood's sufficient statistics scale with the number of observations.

**All this to say: StatLearn lets you do things that would otherwise not be possible at the cost of returning noisy estimates.**

### Algorithms

Besides the loss and penalty functions, you can also plug in a variety of fitting algorithms to `StatLearn`

. Some of these methods are based on the stochastic gradient (gradient of loss evaluated on a single observation). Other methods are based on the majorization-minimization (MM) principle^{[1]}, which offers some guarantees on numerical stability (sometimes at the cost of slower learning).

It is a good idea to test out different algorithms on a sample of your dataset. Plotting the coefficients over time can give you an idea of the stability of the estimates. Use `Trace`

, a wrapper around an OnlineStat, to automatically take equally-spaced snapshots of an OnlineStat's state. Keep in mind the early observations will cause bigger jumps in the cofficients than later observations (based on the learning rate; see Weights. To add further complexity, learning rates (supplied by the `rate`

keyword argument) do not affect each algorithm's learning uniformly. You may need to test different combinations of algorithm/learning rate to find an "optimal" pairing.

```
using OnlineStats, Plots
# fake data
x = rand(Bool, 1000, 10)
y = x * (1:10) + 10randn(1000)
rate = LearningRate(.8)
o = Trace(StatLearn(SGD(), OnlineStats.l2regloss; rate))
o2 = Trace(StatLearn(MSPI(), OnlineStats.l2regloss; rate))
itr = zip(eachrow(x), y)
fit!(o, itr)
fit!(o2, itr)
plot(
plot(o, xlab="Nobs", title="SGD Coefficients", lab=nothing),
plot(o2, xlab="Nobs", title="MSPI Coefficients", lab=nothing),
link=:y
)
```

^{[1]} At the moment, the only place to read about the stochastic MM algorithms in detail is Josh Day's dissertation. Josh is working on an easier-to-digest introduction to these methods and is also happy to discuss them through GitHub issue/email.