SVM-Anova: SVM with univariate feature selection#

This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification scores. We use the iris dataset (4 features) and add 36 non-informative features. We can find that our model achieves best performance when we select around 10% of features.

Load some data to play with#

import numpy as np

from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)

# Add non-informative features
rng = np.random.RandomState(0)
X = np.hstack((X, 2 * rng.random((X.shape[0], 36))))

Create the pipeline#

from sklearn.feature_selection import SelectPercentile, f_classif
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

# Create a feature-selection transform, a scaler and an instance of SVM that we
# combine together to have a full-blown estimator

clf = Pipeline(
        ("anova", SelectPercentile(f_classif)),
        ("scaler", StandardScaler()),
        ("svc", SVC(gamma="auto")),

Plot the cross-validation score as a function of percentile of features#

import matplotlib.pyplot as plt

from sklearn.model_selection import cross_val_score

score_means = list()
score_stds = list()
percentiles = (1, 3, 6, 10, 15, 20, 30, 40, 60, 80, 100)

for percentile in percentiles:
    this_scores = cross_val_score(clf, X, y)

plt.errorbar(percentiles, score_means, np.array(score_stds))
plt.title("Performance of the SVM-Anova varying the percentile of features selected")
plt.xticks(np.linspace(0, 100, 11, endpoint=True))
plt.ylabel("Accuracy Score")
Performance of the SVM-Anova varying the percentile of features selected

Total running time of the script: (0 minutes 0.388 seconds)

Related examples

Univariate Feature Selection

Univariate Feature Selection

Concatenating multiple feature extraction methods

Concatenating multiple feature extraction methods

Plot different SVM classifiers in the iris dataset

Plot different SVM classifiers in the iris dataset

SVM with custom kernel

SVM with custom kernel

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