Manual Recursive Feature Elimination
Now that we've created a diabetes classifier, let's see if we can reduce the number of features without hurting the model accuracy too much.
On the second line of code the features are selected from the original DataFrame. Adjust this selection.
A StandardScaler() instance has been predefined as scaler and a LogisticRegression() one as lr.
All necessary functions and packages have been pre-loaded too.
Cet exercice fait partie du cours
Dimensionality Reduction in Python
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Remove the feature with the lowest model coefficient
X = diabetes_df[['pregnant', 'glucose', 'diastolic', 'triceps', 'insulin', 'bmi', 'family', 'age']]
# Performs a 25-75% train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
# Scales features and fits the logistic regression model
lr.fit(scaler.fit_transform(X_train), y_train)
# Calculates the accuracy on the test set and prints coefficients
acc = accuracy_score(y_test, lr.predict(scaler.transform(X_test)))
print(f"{acc:.1%} accuracy on test set.")
print(dict(zip(X.columns, abs(lr.coef_[0]).round(2))))