1. https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
Let us generate a synthetic data set ( X is the input data matrix and y is the target or label). import
numpy as np n _samples =10 np.random.seed () x=np random.normal(size=n_sanples) y=(x>)
.astype(float) x+.4 * np.random,normal(size=n_samples) (a) [20 points] Apply Logistic Regression
from sklearn to this data set with the regularization parameter C=0.1. Make sure to use scatter plot
to visualize the data set, plot the decision boundary, and predicted probabilities for inputs in the
interval (4,4). For example, you can use the figure in cell 85 of Module3 sildes.pdf or cell 54 of the
textbook's Github page hittpsi/l/github.com/ageronhandson-
mi3/blobimainvo4_training__linear_models.jpynb as a template (b) [20 points] Repeat the same
experiment with the regularization parameter C=105. (c) [10 points] What is the impact of C ? Is it
consistent with the statement in the documentation page for Logistic Regression?
https:/lscikitlearn.orgistable/modulesilineat model.htmliflogistic-regression