# π Wrap-up quizΒΆ

**This quiz requires some programming to be answered.**

Open the dataset `blood_transfusion.csv`

with the following command:

```
import pandas as pd
blood_transfusion = pd.read_csv("../datasets/blood_transfusion.csv")
target_name = "Class"
data = blood_transfusion.drop(columns=target_name)
target = blood_transfusion[target_name]
```

`blood_transfusion`

is a pandas dataframe. The column βClassβ contains the
target variable.

Question

Select the correct answers from the following proposals.

a) The problem to be solved is a regression problem

b) The problem to be solved is a binary classification problem (exactly 2 possible classes)

c) The problem to be solved is a multiclass classification problem (more than 2 possible classes)

d) The proportions of the class counts are imbalanced: some classes have more than twice as many rows than others)

*Select several answers*

Hint: `target.unique()`

, and `target.value_counts()`

are methods
that are helpful to answer to this question.

Question

Using a
`sklearn.dummy.DummyClassifier`

and the strategy `"most_frequent"`

, what is the average of the accuracy scores
obtained by performing a 10-fold cross-validation?

a) ~25%

b) ~50%

c) ~75%

*Select a single answer*

Hint: You can check the documentation of `sklearn.model_selection.cross_val_score`

here
and `sklearn.model_selection.cross_validate`

here.

Question

Repeat the previous experiment but compute the balanced accuracy instead of
the accuracy score. Pass `scoring="balanced_accuracy"`

when calling
`cross_validate`

or `cross_val_score`

functions?

a) ~25%

b) ~50%

c) ~75%

*Select a single answer*

We will use a
`sklearn.neighbors.KNeighborsClassifier`

for the remainder of this quiz.

Question

Why is it relevant to add a preprocessing step to scale the data using a
`StandardScaler`

when working with a `KNeighborsClassifier`

?

a) faster to compute the list of neighbors on scaled data

b) k-nearest neighbors is based on computing some distances. Features need to be normalized to contribute approximately equally to the distance computation.

c) This is irrelevant. One could use k-nearest neighbors without normalizing the dataset and get a very similar cross-validation score.

*Select a single answer*

Create a scikit-learn pipeline (using
`sklearn.pipeline.make_pipeline`

)
where a `StandardScaler`

will be used to scale the data followed by a
`KNeighborsClassifier`

. Use the default hyperparameters.

Question

Inspect the parameters of the created pipeline. What is the value of K, the number of neighbors considered when predicting with the k-nearest neighbors.

a) 1

b) 3

c) 5

d) 8

e) 10

*Select a single answer*

Hint: You can use `model.get_params()`

to get the parameters of a scikit-learn
estimator.

Question

Evaluate the previous model with a 10-fold cross-validation. Use the balanced accuracy as a score. What can you say about this model? Compare the average of the train and test scores to argument your answer.

a) The model clearly underfits

b) The model generalizes

c) The model clearly overfits

*Select a single answer*

Hint: compute the average test score and the average train score and compare
them. Make sure to pass `return_train_score=True`

to the `cross_validate`

function to also compute the train score.

We will now study the effect of the parameter `n_neighbors`

on the train and
test score using a validation curve. You can use the following parameter range:

```
param_range = [1, 2, 5, 10, 20, 50, 100, 200, 500]
```

Also, use a 5-fold cross-validation and compute the balanced accuracy score
instead of the default accuracy score (check the `scoring`

parameter). Finally,
plot the average train and test scores for the different value of the
hyperparameter. We recall that the name of the parameter can be found using
`model.get_params()`

.

Question

Select the true affirmations stated below:

a) The model underfits for a range of

`n_neighbors`

values between 1 to 10b) The model underfits for a range of

`n_neighbors`

values between 10 to 100c) The model underfits for a range of

`n_neighbors`

values between 100 to 500

Question

Select the true affirmations stated below:

a) The model overfits for a range of

`n_neighbors`

values between 1 to 10b) The model overfits for a range of

`n_neighbors`

values between 10 to 100c) The model overfits for a range of

`n_neighbors`

values between 100 to 500

Question

Select the true affirmations stated below:

a) The model best generalizes for a range of

`n_neighbors`

values between 1 to 10b) The model best generalizes for a range of

`n_neighbors`

values between 10 to 100c) The model best generalizes for a range of

`n_neighbors`

values between 100 to 500