# 📝 Exercise M4.01#

The aim of this exercise is two-fold:

understand the parametrization of a linear model;

quantify the fitting accuracy of a set of such models.

We will reuse part of the code of the course to:

load data;

create the function representing a linear model.

## Prerequisites#

### Data loading#

Note

If you want a deeper overview regarding this dataset, you can refer to the Appendix - Datasets description section at the end of this MOOC.

```
import pandas as pd
penguins = pd.read_csv("../datasets/penguins_regression.csv")
feature_name = "Flipper Length (mm)"
target_name = "Body Mass (g)"
data, target = penguins[[feature_name]], penguins[target_name]
```

### Model definition#

```
def linear_model_flipper_mass(
flipper_length, weight_flipper_length, intercept_body_mass
):
"""Linear model of the form y = a * x + b"""
body_mass = weight_flipper_length * flipper_length + intercept_body_mass
return body_mass
```

## Main exercise#

Define a vector `weights = [...]`

and a vector `intercepts = [...]`

of the
same length. Each pair of entries `(weights[i], intercepts[i])`

tags a
different model. Use these vectors along with the vector
`flipper_length_range`

to plot several linear models that could possibly fit
our data. Use the above helper function to visualize both the models and the
real samples.

```
import numpy as np
flipper_length_range = np.linspace(data.min(), data.max(), num=300)
```

```
# Write your code here.
```

In the previous question, you were asked to create several linear models. The visualization allowed you to qualitatively assess if a model was better than another.

Now, you should come up with a quantitative measure which indicates the
goodness of fit of each linear model and allows you to select the best model.
Define a function `goodness_fit_measure(true_values, predictions)`

that takes
as inputs the true target values and the predictions and returns a single
scalar as output.

```
# Write your code here.
```

You can now copy and paste the code below to show the goodness of fit for each model.

```
for model_idx, (weight, intercept) in enumerate(zip(weights, intercepts)):
target_predicted = linear_model_flipper_mass(data, weight, intercept)
print(f"Model #{model_idx}:")
print(f"{weight:.2f} (g / mm) * flipper length + {intercept:.2f} (g)")
print(f"Error: {goodness_fit_measure(target, target_predicted):.3f}\n")
```

```
# Write your code here.
```