Feature importance¶

In this notebook, we will detail methods to investigate the importance of features used by a given model. We will look at:

interpreting the coefficients in a linear model;
the attribute feature_importances_ in RandomForrest;
permutation feature importance, which is an inspection technique that can be used for any fitted model.

0. Presentation of the dataset¶

This dataset is a record of neighborhoods in California district, predicting the median house value (target) given some information about the neighborhoods, as the average number of rooms, the latitude, the longitude or the median income of people in the neighborhoods (block).

from sklearn.datasets import fetch_california_housing
import pandas as pd

X, y = fetch_california_housing(as_frame=True, return_X_y=True)
X.head()

	MedInc	HouseAge	AveRooms	AveBedrms	Population	AveOccup	Latitude	Longitude
0	8.3252	41.0	6.984127	1.023810	322.0	2.555556	37.88	-122.23
1	8.3014	21.0	6.238137	0.971880	2401.0	2.109842	37.86	-122.22
2	7.2574	52.0	8.288136	1.073446	496.0	2.802260	37.85	-122.24
3	5.6431	52.0	5.817352	1.073059	558.0	2.547945	37.85	-122.25
4	3.8462	52.0	6.281853	1.081081	565.0	2.181467	37.85	-122.25

The feature reads as follow:

MedInc median income in block
HouseAge median house age in block
AveRooms average number of rooms
AveBedrms average number of bedrooms
Population block population
AveOccup average house occupancy
Latitude house block latitude
Longitude house block longitude
MedHouseVal Median house value in 100k$ (target)

To assert the quality of our inspection technique, let’s add some random feature that won’t help the prediction (un-informative feature)

import numpy as np

# Adding random features
rng = np.random.RandomState(0)
bin_var = pd.Series(rng.randint(0, 1, X.shape[0]), name='rnd_bin')
num_var = pd.Series(np.arange(X.shape[0]), name='rnd_num')
X_with_rnd_feat = pd.concat((X, bin_var, num_var), axis=1)

We will split the data into training and testing for the remaining part of this notebook

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_with_rnd_feat, y,
                                                    random_state=29)

Let’s quickly inspect some features and the target

import seaborn as sns

train_dataset = X_train.copy()
train_dataset.insert(0, "MedHouseVal", y_train)
_ = sns.pairplot(
    train_dataset[['MedHouseVal', 'Latitude', 'AveRooms', 'MedInc']],
    kind='reg', diag_kind='kde', plot_kws={'scatter_kws': {'alpha': 0.1}})

We see in the upper right plot that the median income seems to be positively correlated to the median house price (the target).

1. Linear model inspection¶

In linear models, the target value is modeled as a linear combination of the features

Coefficients represent the relationship between the given feature $X_i$ and the target $y$, assuming that all the other features remain constant (conditional dependence). This is different from plotting $X_i$ versus $y$ and fitting a linear relationship: in that case all possible values of the other features are taken into account in the estimation (marginal dependence).

from sklearn.linear_model import RidgeCV

model = RidgeCV()

model.fit(X_train, y_train)

print(f'model score on training data: {model.score(X_train, y_train)}')
print(f'model score on testing data: {model.score(X_test, y_test)}')

model score on training data: 0.6079742666388219
model score on testing data: 0.6021994461821603

Our linear model obtains a $R^2$ score of .60, so it explains a significant part of the target. Its coefficient should be somehow relevant. Let’s look at the coefficient learnt

import matplotlib.pyplot as plt

coefs = pd.DataFrame(
   model.coef_,
   columns=['Coefficients'], index=X_train.columns
)

coefs.plot(kind='barh', figsize=(9, 7))
plt.title('Ridge model')
plt.axvline(x=0, color='.5')
plt.subplots_adjust(left=.3)

_images/dev_features_importance_16_0.png

The AveBedrms have the higher coefficient. However, we can’t compare the magnitude of these coefficients directly, since they are not scaled. Indeed, Population is an integer which can be thousands, while AveBedrms is around 4 and Latitude is in degree.

So the Population coefficient is expressed in “$100k\$$ / habitant” while the AveBedrms is expressed in “$100k\$$ / nb of bedrooms” and the Latitude coefficient in “$100k\$$ / degree”.

We see that changing population by one does not change the outcome, while as we go south (latitude increase) the price becomes cheaper. Also, adding a bedroom (keeping all other feature constant) shall rise the price of the house by 80k$.

So looking at the coefficient plot to gauge feature importance can be misleading as some of them vary on a small scale, while others vary a lot more, several decades.

This becomes visible if we compare the standard deviations of our different features.

X_train.std(axis=0).plot(kind='barh', figsize=(9, 7))
plt.title('Features std. dev.')
plt.subplots_adjust(left=.3)
plt.xlim((0, 100))

(0, 100)

_images/dev_features_importance_19_1.png

So before any interpretation, we need to scale each column (removing the mean and scaling the variance to 1).

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

model = make_pipeline(StandardScaler(), RidgeCV())

model.fit(X_train, y_train)

print(f'model score on training data: {model.score(X_train, y_train)}')
print(f'model score on testing data: {model.score(X_test, y_test)}')

model score on training data: 0.6079822261145077
model score on testing data: 0.6015989882624492

coefs = pd.DataFrame(
   model[1].coef_,
   columns=['Coefficients'], index=X_train.columns
)

coefs.plot(kind='barh', figsize=(9, 7))
plt.title('Ridge model')
plt.axvline(x=0, color='.5')
plt.subplots_adjust(left=.3)

_images/dev_features_importance_22_0.png

Now that the coefficients have been scaled, we can safely compare them.

The median income feature, with longitude and latitude are the three variables that most influence the model.

The plot above tells us about dependencies between a specific feature and the target when all other features remain constant, i.e., conditional dependencies. An increase of the HouseAge will induce an increase of the price when all other features remain constant. On the contrary, an increase of the average rooms will induce an decrease of the price when all other features remain constant.

Checking the variability of the coefficients¶

We can check the coefficient variability through cross-validation: it is a form of data perturbation.

If coefficients vary significantly when changing the input dataset their robustness is not guaranteed, and they should probably be interpreted with caution.

from sklearn.model_selection import cross_validate
from sklearn.model_selection import RepeatedKFold

cv_model = cross_validate(
   model, X_with_rnd_feat, y, cv=RepeatedKFold(n_splits=5, n_repeats=5),
   return_estimator=True, n_jobs=-1
)
coefs = pd.DataFrame(
   [model[1].coef_
    for model in cv_model['estimator']],
   columns=X_with_rnd_feat.columns
)
plt.figure(figsize=(9, 7))
sns.swarmplot(data=coefs, orient='h', color='k', alpha=0.5)
# sns.boxplot(data=coefs, orient='h', color='cyan', saturation=0.5)
plt.axvline(x=0, color='.5')
plt.xlabel('Coefficient importance')
plt.title('Coefficient importance and its variability')
plt.subplots_adjust(left=.3)

_images/dev_features_importance_26_0.png

Every coefficient looks pretty stable, which mean that different Ridge model put almost the same weight to the same feature.

Linear models with sparse coefficients (Lasso)¶

In order to illustrate feature selection with a L1 penalty, let’s fit a Lasso model with a strong regularization parameters alpha

from sklearn.linear_model import Lasso

model = make_pipeline(StandardScaler(), Lasso(alpha=.04))

model.fit(X_train, y_train)

print(f'model score on training data: {model.score(X_train, y_train)}')
print(f'model score on testing data: {model.score(X_test, y_test)}')

model score on training data: 0.5599556353946504
model score on testing data: 0.5733210993165778

coefs = pd.DataFrame(
   model[1].coef_,
   columns=['Coefficients'], index=X_train.columns
)

coefs.plot(kind='barh', figsize=(9, 7))
plt.title('Lasso model, strong regularization')
plt.axvline(x=0, color='.5')
plt.subplots_adjust(left=.3)

_images/dev_features_importance_31_0.png

Here the model score is a bit lower, because of the strong regularization. However, it has selectioned only 5 non negative coefficients to make its prediction.

Lessons learned¶

Coefficients must be scaled to the same unit of measure to retrieve feature importance, or comparing them.

Coefficients in multivariate linear models represent the dependency between a given feature and the target, conditional on the other features.

Correlated features might induce instabilities in the coefficients of linear models and their effects cannot be well teased apart.

Inspecting coefficients across the folds of a cross-validation loop gives an idea of their stability.

2. RandomForest `feature_importances_`¶

On some algorithms, there pre-exist some feature importance method, inherently built within the model. It is the case in RandomForrest models. Let’s investigate the built-in feature_importances_ attribute.

from sklearn.ensemble import RandomForestRegressor

model = RandomForestRegressor()

model.fit(X_train, y_train)

print(f'model score on training data: {model.score(X_train, y_train)}')
print(f'model score on testing data: {model.score(X_test, y_test)}')

model score on training data: 0.9747070070081303
model score on testing data: 0.8207325666920497

Contrary to the testing set, the score on the training set is almost perfect, which means that our model is overfitting here.

importances = model.feature_importances_

The importance of a feature is basically: how much this feature is used in each tree of the forest. Formally, it is computed as the (normalized) total reduction of the criterion brought by that feature.

indices = np.argsort(importances)

fig, ax = plt.subplots()
ax.barh(range(len(importances)), importances[indices])
ax.set_yticks(range(len(importances)))
_ = ax.set_yticklabels(np.array(X_train.columns)[indices])

_images/dev_features_importance_39_0.png

Median income is still the most important feature.

It also has a small bias toward high cardinality features, such as the noisy feature rnd_num, which are here predicted having .07 importance, more than HouseAge (which has low cardinality).

3. Feature importance by permutation¶

We introduce here a new technique to evaluate the feature importance of any given fitted model. It basically shuffles a feature and sees how the model changes its prediction. Thus, the change in prediction will be correspond to the feature importance.

# Any model could be used here

model = RandomForestRegressor()
# model = make_pipeline(StandardScaler(),
#                       RidgeCV())

model.fit(X_train, y_train)

print(f'model score on training data: {model.score(X_train, y_train)}')
print(f'model score on testing data: {model.score(X_test, y_test)}')

model score on training data: 0.9746303014141642
model score on testing data: 0.820439473546773

The score on the test set is .81, so feature importance shall be relevant here for this model.

Feature importance¶

Lets compute the feature importance for a given feature, say the MedInc feature.

For that, we will shuffle this specific feature, keeping the other feature as is, and run our same model (already fitted) to predict the outcome. The decrease of the score shall indicate how the model had used this feature to predict the target. The permutation feature importance is defined to be the decrease in a model score when a single feature value is randomly shuffled

For instance, if the feature is crucial for the model, the outcome would also be permuted (just as the feature), thus the score would be close to zero. Afterward, the feature importance is the decrease in score. So in that case, the feature importance would be close to the score.

On the contrary, if the feature is not used by the model, the score shall remain the same, thus the feature importance will be close to 0.

def get_score_after_permutation(model, X, y, curr_feat):
    """ return the score of model when curr_feat is permuted """

    X_permuted = X.copy()
    col_idx = list(X.columns).index(curr_feat)
    # permute one column
    X_permuted.iloc[:, col_idx] = np.random.permutation(
        X_permuted[curr_feat].values)

    permuted_score = model.score(X_permuted, y)
    return permuted_score


def get_feature_importance(model, X, y, curr_feat):
    """ compare the score when curr_feat is permuted """

    baseline_score_train = model.score(X, y)
    permuted_score_train = get_score_after_permutation(model, X, y, curr_feat)

    # feature importance is the difference between the two scores
    feature_importance = baseline_score_train - permuted_score_train
    return feature_importance


curr_feat = 'MedInc'

feature_importance = get_feature_importance(model, X_train, y_train, curr_feat)
print(f'feature importance of "{curr_feat}" on train set is'
      f'{feature_importance:.3}')

feature importance of "MedInc" on train set is0.829

Since there are some randomness, it is advice to run multiple times and inspect the mean and the standard deviation of the feature importance

n_repeats = 10

list_feature_importance = []
for n_round in range(n_repeats):
    list_feature_importance.append(
        get_feature_importance(model, X_train, y_train, curr_feat))

print(
    f'feature importance of "{curr_feat}" on train set is '
    f'{np.mean(list_feature_importance):.3} '
    f'+/- {np.std(list_feature_importance):.3}')

feature importance of "MedInc" on train set is 0.834 +/- 0.00962

0.86 over .97 is very relevant (note the R2 score could go below 0). So we can imagine our model relies heavily on this feature to predict the class. We can now compute the feature permutation importance for all the features

def permutation_importance(model, X, y, n_repeats=10):
    """Calculate importance score for each feature."""

    importances = []
    for curr_feat in X.columns:
        list_feature_importance = []
        for n_round in range(n_repeats):
            list_feature_importance.append(
                get_feature_importance(model, X, y, curr_feat))

        importances.append(list_feature_importance)

    return {'importances_mean': np.mean(importances, axis=1),
            'importances_std': np.std(importances, axis=1),
            'importances': importances}

# This function could directly be access from sklearn
# from sklearn.inspection import permutation_importance

def plot_importantes_features(perm_importance_result, feat_name):
    """ bar plot the feature importance """

    fig, ax = plt.subplots()

    indices = perm_importance_result['importances_mean'].argsort()
    plt.barh(range(len(indices)),
             perm_importance_result['importances_mean'][indices],
             xerr=perm_importance_result['importances_std'][indices])

    ax.set_yticks(range(len(indices)))
    _ = ax.set_yticklabels(feat_name[indices])

Let’s compute the feature importance by permutation on the training data.

perm_importance_result_train = permutation_importance(
    model, X_train, y_train, n_repeats=10)

plot_importantes_features(perm_importance_result_train, X_train.columns)

_images/dev_features_importance_54_0.png

We see again that the feature MedInc, Latitude and Longitude are very important for the model.

We note that our random variable rnd_num is now very less important than latitude. Indeed, the feature importance build-in in RandomForest has bias for continuous data, such as AveOccup and rnd_num.

However, the model still uses these rnd_num feature to compute the output. It is in line with the overfitting we had noticed between the train and test score.

discussion¶

For correlated feature, the permutation could give non realistic sample (e.g. nb of bedrooms higher than the number of rooms)
It is unclear whether you should use training or testing data to compute the feature importance.
Note that dropping a column and fitting a new model will not allow to analyse the feature importance for a specific model, since a new model will be fitted.

Scikit-learn tutorial

Feature importance¶

0. Presentation of the dataset¶

1. Linear model inspection¶

Checking the variability of the coefficients¶

Linear models with sparse coefficients (Lasso)¶

Lessons learned¶

2. RandomForest `feature_importances_`¶

3. Feature importance by permutation¶

Feature importance¶

discussion¶

Take Away¶

Scikit-learn tutorial

Feature importance¶

0. Presentation of the dataset¶

1. Linear model inspection¶

Checking the variability of the coefficients¶

Linear models with sparse coefficients (Lasso)¶

Lessons learned¶

2. RandomForest feature_importances_¶

3. Feature importance by permutation¶

Feature importance¶

discussion¶

Take Away¶

2. RandomForest `feature_importances_`¶