Let's train logistic regression (sklearn.linear_model.LogisticRegression(random_state=0)) on these 2 principal components and y labels to see how well our classes are separated on the plane. Question-: What accuracy_score do you get with logistic regression? Hint: train the model on train set and report accuracy on the test set this time. Use provided split in the cell below. xtr, xte, ytr, yte = train_test_split(digits_2d_pc, y, test_size=0.2, random_state=0)
Let's train logistic regression (sklearn.linear_model.LogisticRegression(random_state=0)) on these 2 principal components and y labels to see how well our classes are separated on the plane. Question-: What accuracy_score do you get with logistic regression? Hint: train the model on train set and report accuracy on the test set this time. Use provided split in the cell below. xtr, xte, ytr, yte = train_test_split(digits_2d_pc, y, test_size=0.2, random_state=0)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
Related questions
Question
MNIST dataset
Handwritten digits from 0 to 9
digits = datasets.load_digits()
X, y = digits['data'], digits['target']
PCA
Now let's try to visualize MNIST with 2 PCA components
pca = PCA(n_components=2, random_state=0)
digits_2d_pc = pca.fit_transform(X)
Do you see any clusters?
Let's train logistic regression (sklearn.linear_model.LogisticRegression(random_state=0)) on these 2 principal components and y labels to see how well our classes are separated on the plane.
Question-: What accuracy_score do you get with logistic regression?
Hint: train the model on train set and report accuracy on the test set this time. Use provided split in the cell below.
xtr, xte, ytr, yte = train_test_split(digits_2d_pc, y, test_size=0.2, random_state=0)
# YOUR CODE HERE
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