You are a machine learning specialist working for a healthcare company where you are building a cancer detection model using a linear regression algorithm. You have gathered your data of hundreds of thousands of patients with over 100 features. However, when you train your model, you notice that it appears to be over-fitting your data.
Which technique can you use to simultaneously correct the over-fitting and reduce your model complexity by removing less relevant features?

Answer options:

A.Use Ridge Regression.
B.Use Lasso Regression.
C.Use Stochastic Gradient Descent.
D.Use a Gaussian Process.

Answer: B
Option A is incorrect. The Ridge Regression approach would reduce the coefficients in your model but not all the way to 0. Therefore, it reduces complexity, but does not entirely eliminate any of the over 100 features in your data.
Option B is correct. The Lasso Regression approach would reduce some of the coefficients in your model to zero, effectively eliminating some of the over 100 features in your data. This will effectively reduce the complexity of your model.
Option C is incorrect. The Stochastic Gradient Descent approach can use a regularization parameter, but it cannot be used to eliminate features from your dataset.
Option D is incorrect. The Gaussian Process approach is used for regression problems, but it does not work well with high dimensional datasets, i.e., over a few dozen features. Your dataset has over 100 features. Also, it cannot be used to eliminate features from your dataset.
Reference:
Please see the Medium article titled Ridge and Lasso Regression: L1 and L2 Regularization (https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b), and the SciKit Learn page titled 1. Supervised learning (https://scikit-learn.org/stable/supervised_learning.html#supervised-learning)