You have applied PCA to a dataset with $10$ numerical features and obtained $10$ principal components. You decide to retain only those principal components that explain at least $90\%$ of the total variance in the data. After doing this, you find that your transformed dataset only has $4$ features. Which of the following is a likely outcome of this dimensionality reduction process?

Group of answer choices

The model will be simpler and faster to train and may also have higher predictive accuracy.

The model will be simpler and faster to train but will have lower predictive accuracy.

The model will be more complex and slower to train but may have higher predictive accuracy.

The model will be more complex and slower to train and may also have lower predictive accuracy.