Assignment1

Explain how Logistic Regression works. Note: Please do it with your own words. Thankyou

I need to plot the data from a simple linear regression with its values x and y with the fitted y values from fitting a logistic regression model. How do I plot the x values with two different y's, y values on the left side with fitted y values on the right? I want to generate the fitted/predicted y values using logistic regression and then create a plot using all those three variables? Here is an example of my SAS code that doesn't work. data coupons;input redeemed discount;cards;100 5147 9176 11211 13244 15277 17310 19343 21;run; ods graphics on;proc logistic data=coupons;model redeemed(event='1') = discount;effectplot;run; It give me this warnings and I do not understand…. NOTE: Option EVENT= is ignored since LINK=CLOGIT. NOTE: PROC LOGISTIC is fitting the cumulative logit model. The probabilities modeled are summed over the responses having the lower Ordered Values in the Response Profile table. Use the response variable option DESCENDING if you want to reverse the assignment of…

In python, for a sample data with 4 columns and 60 rows how do you find the parameters for the regression with the feature map (see attached) where we consider the loss function to be the square of residuals. Once this is done, how do you compute the empirical risk? I've attached some of the data below, it would be sufficient to see how you get results for the question using the above dataset.  1 14 25 620 -1 69 29 625 0 83 27 850 0 28 25 1315 1 41 25 2120 -1 153 31 1315 0 55 25 2600 0 55 31 490 1 69 25 3110 1 83 25 3535

This is a coding question. Now that you have worked out the gradient descent and the update rules. Try to progrum a Ridge regression. Please complete the coding. Note that here the data sct we use has just one explanatory variable and the Ridge regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the observations and the corresponding predictions using Ridge? Now, make a plot to showoase how well your model predicts against the observations. Use scatter plot for observations, line plot for your model predictions. Observations are in color red. and prodictions are in color green. Add appropriate labeis to the x axis and y axis and a title to the plot You may also need to fine tune hyperparameters such as Icurning rate and the number ofliterations.

Model evaluation Create a predictions variable using your fitted model and the test dataset; call it y_pred. Then get the accuracy score of your predictions and save it in a variable called accuracy. Finally get the confusion matrix for your predictions and save it in a variable called confusion_mat. Code: y_pred = Noneaccuracy = Noneconfusion_mat = None

Go to UCI data repository (https://archive.ics.uci.edu/ml/datasets.php) and identify two data sets, one for estimation/regression and one for classification. Using Microsoft Excel or any other statistical software: a. Develop and interpret a linear regression model. b. Develop and interpret a logistic regression model. Note: Answers must be in your own words.

Python regression b) When adding more variables to a linear model, what is true about the R-squared value? c) Adding more variables always leads to a lower test MSE.FalseTrue

Explain the flaws in this model training strategy. What's your solution? We want to create a hip X-Ray deformity prediction model. 100 individuals have 640 frontal X-rays. Three orthopedic physicians label the photos as positive or negative for hip deformity. The picture dataset was randomly divided among 80% training (training and validation) and 20% testing.

This is a coding question. Now that you have worked out the gradient descent and the update rules. "Try to progrum a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridge regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the observations and the corresponding predictions using Ridge? Now, make a plot to showease how well your model predicts against the observations. Use spatter plot for observations, line plot for your model predictions. Observations are in color red. and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot You may also nood to fine tune hyperparameters such as leurning rate and the number of'aterations.

This is a coding question. Try to program a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridgc regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the obscrvations and the corresponding predictions using Ridge? Now, make a plot to showoase haw well your model predicts against the observations. Use scatter plot for observations, line plot for your model predictions. Observations are in color red, and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot. You may also need to tine tune hyperparamoters such as learning rate and the number of iterations. side note- make sure the code runs successfully

Compared to stepwise regression, all-subsets regression offers many advantages.

Write code to define a K-NN regression model with K=5 and the neighbors are weighted by the inverse of their distance. [ ] # Write your code here

population_df = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/AnalyseProject/world_population.csv', index_col='Country Code') Question 1: Population Growth   The world population data spans from 1960 to 2017. We'd like to build a predictive model that can give us the best guess at what the population growth rate in a given year might be. We will calculate the population growth rate as follows:-   ?????ℎ_????=???????_????_??????????−????????_????_??????????????????_????_??????????     As such, we can only calculate the growth rate for the year 1961 onwards. Write a function that takes the population_df and a country_code as input and computes the population growth rate for a given country starting from the year 1961. This function must return a return a 2-d numpy array that contains the year and corresponding growth rate for the country. Function Specifications: Should take a population_df and country_code string as input and return a numpy array…

population_df = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/AnalyseProject/world_population.csv', index_col='Country Code')   Question 1: Population Growth   The world population data spans from 1960 to 2017. We'd like to build a predictive model that can give us the best guess at what the population growth rate in a given year might be. We will calculate the population growth rate as follows:- The formula to use in calculating the growth rate is as below:- Growth_rate =( Current_year_ population  - Previous_year_population) / Previous_year_population     As such, we can only calculate the growth rate for the year 1961 onwards. Write a function that takes the population_df and a country_code as input and computes the population growth rate for a given country starting from the year 1961. This function must return a return a 2-d numpy array that contains the year and corresponding growth rate for the country. Function Specifications: Should take…

In this assignment you will implement linear regression model and evaluate their performance on the California house price data set. (housing.csv) Apply the codes and save them in a seperate .py or .ipynb file DO NOT PUT THE CODE IN YOUR REPORT DOCUMENT, only present your output metrics as well as requested graphs and personal comments in the report. Name the report and code files with surname_studentID_section. You will submit a report and a .py/.ipynb file. Only use the data set version provided with the assignment do not download other versions or use the ready made version in google colab. In the assignment you will do the following: - apply linear regression on each individual numerical feature (drop features : 'ocean_proximity' ‘longitude', 'latitude') - output the coefficients and your self implemented error measures: sum of squared error SSE, mean squared error MSE, use split percentage cross validation with 30% test size and shuffle as True refer to documentations during…

What is the type of method to create regression models in which the coefficients are penalized for being too large than what they should be if multicollinearity was not there? Answer Choices: a) Elastic Net b) Lasso c) Ridge d) Regularization

Answer the questions below within 50 minutes. Use Google Colaboratory and submit your answer in an “ipynb” file.

python

Plot following curves in the SAME figure where x-axis is "Model Complexity" and y-axis is the value: (1) Bias; (2) Variance; (3) Training Accuracy; (4) Testing Accuracy; [Paste your plot here]

This is a coding question. Try to progrum a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridge regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the observations and the corresponding predictions using Ridge? Now, make a plot to showcase how well your model predicts against the observations. Use scatter plot tor observations, line plot for your model predictions. Observations are in color red, and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot. You may also need to fine tune hyperparameters such as leurning rate and the number of iterations.

Build A Machine Learning Regression Model with Python and TensorFlow for : • One Variable • Multiple inputs : A DNN regression • One variable • Full model

Write a computer code to do linear regression analysis of a given dataset to find the relation between two variables which gives the least sum of squares error. Using Excel or Matlab. Include a copy of the script, a sample input, and a sample output from your codes. Sample output must include modelfit parameters and the sum of the squared errors for the fit. Please also run the code for thefollowing data set to find and report the relation between y and x:x y25 3040 80120 15075 80150 200300 350270 240400 320450 470575 583

First, perform the following tasks: • Make a linear regression model with all the features in the dataset. Use train_test_split to keep 20% of the data for testing. • Use your model to predict values for test set and print the predictions for the first 10 instances of the test data and compare them with actual values. • Print the coefficient values and their corresponding feature name (e.g. age 43, bmi 200, .) • Note that you can access feature_names from diabetes dataset directly • Calculate training-MSE, testing-MSE, and R-squared value. Compare the two models. Did using all available features improve the performance? In [ ]: # Your code goes here In [ ]: # Your code goes here

a. Feature selection can be done through both filter and wrapper method. Which of these methods is more accurate and which of these is more efficient? Explain the tradeoff and justify your answer with an example. b. Why in some situations logistic regression model is preferred over linear regression?

Which Python module and method are used to create a multiple regression model for a given data set? Select one. linregress method from scipy module linregress method from statsmodel module ols method from scipy module ols method from statsmodel module

To run any stats you must first import statistics   True   False    2. For relatively low-level stats, it generally doesn't matter if you run them in statsmodels, the statistics library, or the other ways we saw     True   False   3. By default, scikit-learn and statsmodels create equivalent logistic regression models   True   False 4. which of these might you want to add to the default statmodels regression model?   Constant   Multiple comparisons   Scatterplot     5.Which of the following is how we would create a logistic regression model?     sm.OLS   sm.Logit()   sm.LogReg()   sm.Logistic()   6.Which of the following are common ways of installing packages?     conda   pip   install.py   install.packages()…

The lower the misclassification rate, the better the predictive performance of a logistic regression model. True False PLEASE ANSWER CORRECTLY

What is a stepwise regression?