An introduction to statistical learning: with applications in R
13th Edition
ISBN: 9781461471387
Author: James, Gareth, Witten, Daniela, Hastie, Trevor, TIBSHIRANI, Robert
Publisher: MPS (CC)
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Chapter 2, Problem 7E
a.
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Euclidean distance
X1 | X2 | X3 | Y | Distance from origin |
0 | 3 | 0 | Red | 3 |
2 | ... |
b.
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Prediction of value k
- Prediction with K=1 is Green...
c.
Explanation of Solution
Prediction of value k
- Prediction with K=1 is Green.
- This is because t...
d.
Explanation of Solution
Bayes decision boundary
- When K becomes larger, we get a smoother boundary...
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An introduction to statistical learning: with applications in R
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- Considering the threshold as 0.5, Calculate the F1 measure for attached predictions of a classification model. Group of choice: A. 1 B. 0.45 C. 0.67 D.0.53arrow_forward2. Can you design a binary classification experiment with 100 total population (TP+TN+FP+ FN), with precision (TP/(TP+FP)) of 1/2, with sensitivity (TP/(TP+FN)) of 2/3, and specificity (TN/(FP+TN)) of 3/5? (Please consider the population to consist of 100 individuals.)arrow_forward2. Take a bivariate normal distribution with two random variables X and Y, with mean value = (1, -1), var(X) = 3, var(Y) = 6, and cor(X,Y) = -0.5. %3! (a) create a contour plot for this data (b) plot 1,000 simulations of this distribution (c) Using 1,000,000 simulations, find (1) the expected value of Y (ii) the expected value of Y, given that X> 2 (ii) the expected value of Y, given that X = 2arrow_forward
- A histogram is plotted to get an idea of the probability distribution for a feature in a dataset. Given the histogram, what would you estimate for the probability that, for a random sample, the feature lies between 2 and 4? 0.175 0.150 0.125 0.100 0.075 0.050 0.025 0.000 0.5 0.125 0.05 0 0.25 -2 -6 8 10arrow_forwardgiven the observed data (obsX,obsY), learning rate (alpha), error change threshold, and delta from the huber loss model,write a function returns theta0 and theta1 that minimizes the error. Use pseudo huber loss functionarrow_forwardExercise 10 Of the sampling distributions from 2 and 3, which has a smaller spread? If you're concerned with making estimates that are more often close to the true value, would you prefer a sampling distribution with a large or small spread?arrow_forward
- 1 Change this code from Matlab to Phython: function p = predict (theta, X) % PREDICT Predict whether the label is 0 or 1 using learned logistic 5 åregression parameters theta 4 p = PREDICT (theta, X) computes the predictions for X using a threshold at 0.5 (i.e., if sigmoid (theta'*x) >= 0.5, predict 1) size (X, 1); % Number of training examples % You need to return the following variables correctly zeros (m, 1); p=sigmoid (X*theta); 8 m = 9. 10 11 12 for i=1:m if (p (i) >= 0.5) p(i) =1; 13 14 15 else 16 p(i)=0; 17 end 18 end 19 end olo oto oto oto olo olo olo oto olo olo oto olo olo olo oto olo olo olo olo olo olo ofo o1o olo H23 +56 7arrow_forwardThe table below shows the prediction results from Logistic Regression, which gives results in the range 0 − 1, find the TP, TN, FP, FN, TPR, and FPR for each threshold from 0.1 to 0.9arrow_forwardWhen building a predictive model, out-of-sample predictive accuracy will always improve when we include any independent variable that leads to an increase in the R-Square. TRUE FALSEarrow_forward
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