# Weights

In machine learning it is possible to assign each observation an independent significance, or *weight*, either in **training** or in **performance evaluation**, or both.

There are two kinds of weights in use in MLJ:

*per observation weights*(also just called*weights*) refer to weight vectors of the same length as the number of observations*class weights*refer to dictionaries keyed on the target classes (levels) for use in classification problems

## Specifying weights in training

To specify weights in training you bind the weights to the model along with the data when constructing a machine. For supervised models the weights are specified last:

```
KNNRegressor = @load KNNRegressor
model = KNNRegressor()
X, y = make_regression(10, 3)
w = rand(length(y))
mach = machine(model, X, y, w) |> fit!
```

Note that `model`

supports per observation weights if `supports_weights(model)`

is `true`

. To list all such models, do

```
models() do m
m.supports_weights
end
```

The model `model`

supports class weights if `supports_class_weights(model)`

is `true`

.

## Specifying weights in performance evaluation

When calling an MLJ measure (metric) that supports weights, provide the weights as the last argument, as in

```
_, y = @load_iris
ŷ = shuffle(y)
w = Dict("versicolor" => 1, "setosa" => 2, "virginica"=> 3)
macro_f1score(ŷ, y, w)
```

You can use `supports_weights`

and `supports_class_weights`

on measures to check weight support. For example, to list all measures supporting per observation weights, do

```
measures() do m
m.supports_weights
end
```

See also Evaluating Model Performance.

To pass weights to all the measures listed in an `evaluate!/evaluate`

call, use the keyword specifiers `weights=...`

or `class_weights=...`

. For details, see `evaluate!`

.