Exercise

# A random model

In this exercise you will reconstruct the cumulative gains curve's baseline, that is, the cumulative gains curve of a random model.

To do so, you need to construct random predictions. The `plot_cumulative_gain`

method requires two values for these predictions: one for the target to be 0 and one for the target to be 1. These values should sum to one, so a valid list of predictions could for instance be `[(0.02,0.98),(0.27,0.73),...,(0.09,0.91)]`

.

In Python, you can generate a random value between values `a`

and `b`

as follows:

```
import random
random_value = random.uniform(a,b)
```

Instructions

**100 XP**

- Import the
`random`

,`matplotlib`

and`scikitplot`

modules - Construct a list
`random_predictions`

that contains random numbers between 0 and 1. - Adjust the list
`random_predictions`

such that it contains tuples`(r,a)`

with`r`

the original value of the list and`a`

such that \(r+a=1\). - The true values of the target are in
`targets_test`

. Show the cumulative gains graph of your random model.