g features (X_train), training labels (y_train), testing features (X_test) and testing labels (y_test)
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Hey guys can I get help with the following question:
Function 6
Write a function to return the number of false negatives (as an integer) of a logistic regression model
- Given the training features (X_train), training labels (y_train), testing features (X_test) and testing labels (y_test)
### START FUNCTION 6
def log_reg_fn(X_train, y_train, X_test, y_test):
# YOUR CODE HERE
### END FUNCTION 6
log_reg_fn(X_train_c, y_train_c, X_test_c, y_test_c)
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- Run a regression predicting bill from temperature, pizzas, and got_wine. Assign the standardized regression coefficients to Q4 by using the lm.beta() function. You should not round these values. The output should look something like this: -0.30580803 0.25976246 0.08161478 Note: CodeGrade will be grading you based on the last line (just the values). Add operator to the regression from Q4. Which is the better model? Assign the better model’s AIC to Q5. Use the classical AIC (k=2). Answe should look like this:9373.33I 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…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
- Please help with the artificial intelligence question below thanks! Given a number of data samples (X, Class) in the attached file where each data sample consists of a variable X and a Class whose value is 1 or 2. (1) Using the given sample data, use the Gradient Descent algorithm to predict the logistic regression model (Note: the logistic regression model is NOT a regression model). (2) Using the logistic regression model as a solution to point (1) above, predict the Class of a sample that has a value of X = 5.69. Packages outside of base Python never come with any Python installation. True False 10. ANOVA is an omnibus test True False 11. OLS can be used for regression and ANOVA True False 12.The following will compare differences in traffic across days mulicomp.pairwise_turkeyhsd(df.day, df.trafic) True False 13. Statsmodels generally requires ___ matrices statsmodels doesn't require matrices 4 2 3 14. Match the following Independent/predictor variables Dependent/response variables 1. Endogenous 2. Exogenous 15. Boxplots are useful in comparing mean differences across groups True False 16. Scatterplots are useful for comparing mean differences across groups True FalsePlease help with the ai question below, thanks Given a number of data samples (X, Class) in the attached file where each data sample consists of a variable X and a Class whose value is 1 or 2. (1) Using the given sample data, use the Gradient Descent algorithm to predict the logistic regression model (Note: the logistic regression model is NOT a regression model). (2) Using the logistic regression model as a solution to point (1) above, predict the Class of a sample that has a value of X = 5.6
- What is the code in Stata for this question? Run the linear regression given by wages = β0 +β1education+β2workexp+β3unionmember +β4south+ β5−9occupation + β10female + u where wages=hourly wage in US dollars; education=years of schooling; workexp=years of work experience; unionmember=a dummy variable equal to ”1” if a person is a union member, and ”0” otherwise; south=a dummy variable equal to ”1” if a person lives in the south, and ”0” otherwise; occupation=a categorical variable equal to ”1” if a person’s occupation is ”management”; ”2” if it is ”sales”; ”3” if it is ”clerical”, ”4” if it is ”service”, ”5” if it is ”professional” and ”6” if it is ”other”. Please make sure to use ”other” as the base category; female =a dummy variable equal to ”1” if a person is female, and ”0” if a person is male.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 3535The ols() method in statsmodels is used to fit a simple linear regression model using “Exam4” as the response variable and “Exam3” as the predictor variable. The output is shown below. A text version is available. What is the correct regression equation based on this output? Is this model statistically significant at 10% level of significance (alpha = 0.10)? Select one. (Hint: Review results of F-statistic)
- 3You are expected to train a Linear Regression model on any given data. After you have trained a linear regressor model. Apply Principal Component Analysis on the given dataset. Again, train a linear regressor model on the data with lesser dimensions.You are expected to write the code for the linear regression model, principal component analysis by using numpy and python.TODO: Lienar Regression with least Mean Squares (LMS) Optimize the model through gradient descent. *Please complete the TODOs. * !pip install wget import osimport randomimport tracebackfrom pdb import set_traceimport sysimport numpy as npfrom abc import ABC, abstractmethodimport traceback from util.timer import Timerfrom util.data import split_data, feature_label_split, Standardizationfrom util.metrics import msefrom datasets.HousingDataset import HousingDataset class BaseModel(ABC): """ Super class for ITCS Machine Learning Class""" @abstractmethod def fit(self, X, y): pass @abstractmethod def predict(self, X): pass class LinearModel(BaseModel): """ Abstract class for a linear model Attributes ========== w ndarray weight vector/matrix """ def __init__(self): """ weight vector w is initialized as None """ self.w = None # check if the matrix is 2-dimensional. if…What are Ensemble Algorithms? Write an algorithm for BOOSTING method. Assume that two individuals offer to sell you their predictive models M1 and M2. The confusion matrices produced by each model are as follows. What is the accuracy of each model? Assuming that precision is of paramount importance in your application, which of the two models would you buy? Why? Performance of M1 Performance of M2 Predicted True Predicted False Predicted True Predicted False Actually True 5 95 Actually True 85 15 Actually False 10 90 Actually False 95 5
