Exercise

# Scaling up to multiple data points

You've seen how different weights will have different accuracies on a single prediction. But usually, you'll want to measure model accuracy on many points. You'll now write code to compare model accuracies for two different sets of weights, which have been stored as `weights_0`

and `weights_1`

.

`input_data`

is a list of arrays. Each item in that list contains the data to make a single prediction.
`target_actuals`

is a list of numbers. Each item in that list is the actual value we are trying to predict.

In this exercise, you'll use the `mean_squared_error()`

function from `sklearn.metrics`

. It takes the true values and the predicted values as arguments.

You'll also use the preloaded `predict_with_network()`

function, which takes an array of data as the first argument, and weights as the second argument.

Instructions

**100 XP**

- Import
`mean_squared_error`

from`sklearn.metrics`

. - Using a
`for`

loop to iterate over each row of`input_data`

:- Make predictions for each row with
`weights_0`

using the`predict_with_network()`

function and append it to`model_output_0`

. - Do the same for
`weights_1`

, appending the predictions to`model_output_1`

.

- Make predictions for each row with
- Calculate the mean squared error of
`model_output_0`

and then`model_output_1`

using the`mean_squared_error()`

function. The first argument should be the actual values (`target_actuals`

), and the second argument should be the predicted values (`model_output_0`

or`model_output_1`

).