nk to csv file : https://www.dropbox.com/s/umxdnzxsp44gg5g/ex32_data.csv?dl=0 link for required functions to solve question b) : https://www.dropbox.com/s/l0xbnf1yuk1nix8/Chapter10-LogisticRegression.pd
nk to csv file : https://www.dropbox.com/s/umxdnzxsp44gg5g/ex32_data.csv?dl=0 link for required functions to solve question b) : https://www.dropbox.com/s/l0xbnf1yuk1nix8/Chapter10-LogisticRegression.pd
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Question
- link to csv file : https://www.dropbox.com/s/umxdnzxsp44gg5g/ex32_data.csv?dl=0
- link for required
functions to solve question b) : https://www.dropbox.com/s/l0xbnf1yuk1nix8/Chapter10-LogisticRegression.pdf?dl=0
![Download the associated file' from E-Learning. In this exercise we will try to predict whether
a person has Diabetes using logistic regression.
a) Pre-process the data as follows. First, read the csv file, then divide the columns into
two types of variables: The target variable (also called dependent variable) is the last
column, which you should store in the variable y. The feature variables (also called
independent variables) are all other columns. Store these in the variable X.
Next, as in the lecture, divide X and y into training data (train_set_x, train_set_y)
and test data (test_set_x, test_set_y) using 75% of the data as training data and
the other 25% as test data.
b) Copy all the functions that you need for logistic regression from the lecture notes (in-
cluding initialize_parameters(dim)) and modify the following: In the propagate
function, after the cost is calculated, check whether it is NaN and if so, change it to
np.inf. In the optimize function, every 10000 steps (instead of every 100 steps),
append the cost to the costs list and output the quadratic Euclidean norm of the
gradient of the cost: np. sum (grads ['dw'] ** 2)+ np.sum(grads ['db'] ** 2)
c) As in the lecture notes, call the model function to run the logistic regression and then
plot the costs.
Important: Set np.random.seed (0) before the pre-processing step. When calling the
model function, set the number of steps to 1000001 and the learning rate to 0.00025.
Hints: When calling the model function, you might need to fix any errors regarding
the dimension of arrays. You can do so by comparing the shapes of the respective
arrays to the ones from the lecture.
d) Output the so-called confusion matrix. It displays the number of correct predictions
on the diagonal and the incorrect predictions on the off-diagonal, similar to
Predicted
1
118
12
26
36
To output it, proceed as follows: First, import metrics from the sklearn module:
from sklearn import metrics. Then use predict to calculate the predictions on the
test set. Next, call cnf_matrix = metrics.confusion_matrix(arg1, arg2),
you should replace arg1 by the test set of y and arg2 by the predictions on that test
set. Finally, print(cnf_matrix).
where
e) Perform the tasks c) and d) with
• a learning rate of 0.00025, once with 100001 steps and once with 10001 steps,
• a learning rate of 0.0002, once with 1000001 steps, once with 100001 steps, and
once with 10001 steps,
and compare the corresponding confusion matrices.
Actual](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fffcba5dd-0a65-44c1-9fe6-5daab1c742bf%2Fc2bf3c90-e867-4aff-b638-e623bbf50241%2Fni07bqz_processed.png&w=3840&q=75)
Transcribed Image Text:Download the associated file' from E-Learning. In this exercise we will try to predict whether
a person has Diabetes using logistic regression.
a) Pre-process the data as follows. First, read the csv file, then divide the columns into
two types of variables: The target variable (also called dependent variable) is the last
column, which you should store in the variable y. The feature variables (also called
independent variables) are all other columns. Store these in the variable X.
Next, as in the lecture, divide X and y into training data (train_set_x, train_set_y)
and test data (test_set_x, test_set_y) using 75% of the data as training data and
the other 25% as test data.
b) Copy all the functions that you need for logistic regression from the lecture notes (in-
cluding initialize_parameters(dim)) and modify the following: In the propagate
function, after the cost is calculated, check whether it is NaN and if so, change it to
np.inf. In the optimize function, every 10000 steps (instead of every 100 steps),
append the cost to the costs list and output the quadratic Euclidean norm of the
gradient of the cost: np. sum (grads ['dw'] ** 2)+ np.sum(grads ['db'] ** 2)
c) As in the lecture notes, call the model function to run the logistic regression and then
plot the costs.
Important: Set np.random.seed (0) before the pre-processing step. When calling the
model function, set the number of steps to 1000001 and the learning rate to 0.00025.
Hints: When calling the model function, you might need to fix any errors regarding
the dimension of arrays. You can do so by comparing the shapes of the respective
arrays to the ones from the lecture.
d) Output the so-called confusion matrix. It displays the number of correct predictions
on the diagonal and the incorrect predictions on the off-diagonal, similar to
Predicted
1
118
12
26
36
To output it, proceed as follows: First, import metrics from the sklearn module:
from sklearn import metrics. Then use predict to calculate the predictions on the
test set. Next, call cnf_matrix = metrics.confusion_matrix(arg1, arg2),
you should replace arg1 by the test set of y and arg2 by the predictions on that test
set. Finally, print(cnf_matrix).
where
e) Perform the tasks c) and d) with
• a learning rate of 0.00025, once with 100001 steps and once with 10001 steps,
• a learning rate of 0.0002, once with 1000001 steps, once with 100001 steps, and
once with 10001 steps,
and compare the corresponding confusion matrices.
Actual
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