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.
Este exercício faz parte do curso
Dimensionality Reduction in Python
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# 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))))