# π Solution for Exercise M7.01#

In this exercise we will define dummy classification baselines and use them as reference to assess the relative predictive performance of a given model of interest.

We illustrate those baselines with the help of the Adult Census dataset, using only the numerical features for the sake of simplicity.

```
import pandas as pd
adult_census = pd.read_csv("../datasets/adult-census-numeric-all.csv")
data, target = adult_census.drop(columns="class"), adult_census["class"]
```

First, define a `ShuffleSplit`

cross-validation strategy taking half of the
samples as a testing at each round. Let us use 10 cross-validation rounds.

```
# solution
from sklearn.model_selection import ShuffleSplit
cv = ShuffleSplit(n_splits=10, test_size=0.5, random_state=0)
```

Next, create a machine learning pipeline composed of a transformer to standardize the data followed by a logistic regression classifier.

```
# solution
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
classifier = make_pipeline(StandardScaler(), LogisticRegression())
```

Compute the cross-validation (test) scores for the classifier on this dataset. Store the results pandas Series as we did in the previous notebook.

```
# solution
from sklearn.model_selection import cross_validate
cv_results_logistic_regression = cross_validate(
classifier, data, target, cv=cv, n_jobs=2
)
test_score_logistic_regression = pd.Series(
cv_results_logistic_regression["test_score"], name="Logistic Regression"
)
test_score_logistic_regression
```

```
0 0.815937
1 0.813849
2 0.815036
3 0.815569
4 0.810982
5 0.814709
6 0.813112
7 0.810327
8 0.812416
9 0.816388
Name: Logistic Regression, dtype: float64
```

Now, compute the cross-validation scores of a dummy classifier that constantly predicts the most frequent class observed the training set. Please refer to the online documentation for the sklearn.dummy.DummyClassifier class.

Store the results in a second pandas Series.

```
# solution
from sklearn.dummy import DummyClassifier
most_frequent_classifier = DummyClassifier(strategy="most_frequent")
cv_results_most_frequent = cross_validate(
most_frequent_classifier, data, target, cv=cv, n_jobs=2
)
test_score_most_frequent = pd.Series(
cv_results_most_frequent["test_score"],
name="Most frequent class predictor",
)
test_score_most_frequent
```

```
0 0.760329
1 0.756808
2 0.759142
3 0.760739
4 0.761681
5 0.761885
6 0.757463
7 0.757176
8 0.761885
9 0.763114
Name: Most frequent class predictor, dtype: float64
```

Now that we collected the results from the baseline and the model, concatenate the test scores as columns a single pandas dataframe.

```
# solution
all_test_scores = pd.concat(
[test_score_logistic_regression, test_score_most_frequent],
axis="columns",
)
all_test_scores
```

Logistic Regression | Most frequent class predictor | |
---|---|---|

0 | 0.815937 | 0.760329 |

1 | 0.813849 | 0.756808 |

2 | 0.815036 | 0.759142 |

3 | 0.815569 | 0.760739 |

4 | 0.810982 | 0.761681 |

5 | 0.814709 | 0.761885 |

6 | 0.813112 | 0.757463 |

7 | 0.810327 | 0.757176 |

8 | 0.812416 | 0.761885 |

9 | 0.816388 | 0.763114 |

Next, plot the histogram of the cross-validation test scores for both models with the help of pandas built-in plotting function.

What conclusions do you draw from the results?

```
# solution
import numpy as np
import matplotlib.pyplot as plt
bins = np.linspace(start=0.5, stop=1.0, num=100)
all_test_scores.plot.hist(bins=bins, edgecolor="black")
plt.legend(bbox_to_anchor=(1.05, 0.8), loc="upper left")
plt.xlabel("Accuracy (%)")
_ = plt.title("Distribution of the CV scores")
```

We observe that the two histograms are well separated. Therefore the dummy
classifier with the strategy `most_frequent`

has a much lower accuracy than
the logistic regression classifier. We conclude that the logistic regression
model can successfully find predictive information in the input features to
improve upon the baseline.

Change the `strategy`

of the dummy classifier to `"stratified"`

, compute the
results. Similarly compute scores for `strategy="uniform"`

and then the plot
the distribution together with the other results.

Are those new baselines better than the previous one? Why is this the case?

Please refer to the scikit-learn documentation on
sklearn.dummy.DummyClassifier
to find out about the meaning of the `"stratified"`

and `"uniform"`

strategies.

```
# solution
stratified_dummy = DummyClassifier(strategy="stratified")
cv_results_stratified = cross_validate(
stratified_dummy, data, target, cv=cv, n_jobs=2
)
test_score_dummy_stratified = pd.Series(
cv_results_stratified["test_score"], name="Stratified class predictor"
)
```

```
uniform_dummy = DummyClassifier(strategy="uniform")
cv_results_uniform = cross_validate(
uniform_dummy, data, target, cv=cv, n_jobs=2
)
test_score_dummy_uniform = pd.Series(
cv_results_uniform["test_score"], name="Uniform class predictor"
)
```

```
all_test_scores = pd.concat(
[
test_score_logistic_regression,
test_score_most_frequent,
test_score_dummy_stratified,
test_score_dummy_uniform,
],
axis="columns",
)
```

```
all_test_scores.plot.hist(bins=bins, edgecolor="black")
plt.legend(bbox_to_anchor=(1.05, 0.8), loc="upper left")
plt.xlabel("Accuracy (%)")
_ = plt.title("Distribution of the test scores")
```

We see that using `strategy="stratified"`

, the results are much worse than
with the `most_frequent`

strategy. Since the classes are imbalanced,
predicting the most frequent involves that we will be right for the proportion
of this class (~75% of the samples). However, the `"stratified"`

strategy will
randomly generate predictions by respecting the training setβs class
distribution, resulting in some wrong predictions even for the most frequent
class, hence we obtain a lower accuracy.

This is even more so for the `strategy="uniform"`

: this strategy assigns class
labels uniformly at random. Therefore, on a binary classification problem, the
cross-validation accuracy is 50% on average, which is the weakest of the three
dummy baselines.

Note: one could argue that the `"uniform"`

or `strategy="stratified"`

strategies are both valid ways to define a βchance levelβ baseline accuracy
for this classification problem, because they make predictions βby chanceβ.

Another way to define a chance level would be to use the
sklearn.model_selection.permutation_test_score
utility of scikit-learn. Instead of using a dummy classifier, this function
compares the cross-validation accuracy of a model of interest to the
cross-validation accuracy of this same model but trained on randomly permuted
class labels. The `permutation_test_score`

therefore defines a chance level
that depends on the choice of the class and hyper-parameters of the estimator
of interest. When training on such randomly permuted labels, many machine
learning estimators would end up approximately behaving much like the
`DummyClassifier(strategy="most_frequent")`

by always predicting the majority
class, irrespective of the input features. As a result, this `"most_frequent"`

baseline is sometimes called the βchance levelβ for imbalanced classification
problems, even though its predictions are completely deterministic and do not
involve much βchanceβ anymore.

Defining the chance level using `permutation_test_score`

is quite
computation-intensive because it requires fitting many non-dummy models on
random permutations of the data. Using dummy classifiers as baselines is often
enough for practical purposes. For imbalanced classification problems, the
`"most_frequent"`

strategy is the strongest of the three baselines and
therefore the one we should use.