The Iris dataset has 3 species and 4 features: sepal length, sepal width, petal length, and petal width. Analysis shows:

• Petal length and petal width are strongly positively correlated.
• Sepal length and petal length are moderately correlated.

Based on this, which statements are correct?

A) Including both petal length and petal width may introduce redundancy and does not necessarily improve model performance.

B) For Linear Discriminant Analysis (LDA), highly correlated features can cause the within-class covariance matrix to become ill-conditioned, reducing model stability.

C) Moderate correlation between sepal length and petal length means these features are completely independent.

D) Strongly correlated features are always preferred for any machine learning model because they increase predictive power.