Use R programming only for part A, B-E should be by hand please 4-The following is a training dataset that has ten one dimensional objects.Person idHome ownerRefund1YesTRUE2NoTRUE3YesFALSE4NoFALSEYesFALSENoTRUE7NOTRUENOFALSE9.NOFALSE10YESFALSEA. Create ten bootstrap samples from the above training dataset. (You should use R toextract bootstrap samples. Use person id to extract the samples)B. Build a decision stump (One level decision tree that split the root using entropy) foreach bootstrap sample to predict if each object/person gets refund based on the maritalstatus attribute.C. Apply the decision stumps on the original dataset to predict a class label (refund) foreach objects in the original data set.D. Determine the final prediction for each object using majority of votes.E. Find the training error of your model.*You should not use R for section B,C,D, and E - Please show your works

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-The following is a training dataset that has ten on Person id Home owner Refund Yes TRUE No TRUE Yes FALSE No FALSE

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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pizzaStats Assignment Description For this assignment, name your R file pizzaStats.R For all questions you should load tidyverse and lm.beta. You should not need to use any other libraries. Load tidyverse with suppressPackageStartupMessages(library(tidyverse)) Load lm.beta withsuppressPackageStartupMessages(library(lm.beta)) Run a regression predicting whether or not wine was ordered from temperature, bill, and pizza. Assign the coefficients of the summary of the model to Q1. The output should look something like this: Estimate Std. Error z value Pr(>|z|)(Intercept) -1.09 1.03 -1.06 0.29temperature -0.04 0.01 -3.20 0.00bill 0.03 0.01 3.75 0.00pizzas 0.19 0.06 3.27 0.00 Here is the link of the pizza.csv https://drive.google.com/file/d/1_m2TjPCkPpMo7Z2Vkp32NiuZrXBxeRn6/view?usp=sharing
Please help me get the outputs
Which statement(s) are false about Map?* It cannot contain duplicate keys and each key can map to at most one value. It is an object that maps keys to values, or is a collection of attribute-value pairs. It can map a key to a function. A Map is an object that maps functions to values, or is a collection of attribute-value pairs.
complete the docstring using the information provided:
using c++, Create a Student class, where attributes associated with each student are name, registrationnumber, father name, degree and department. All attributes should not be accessed directly.One can view the details of all students. Note student attributes can not be changed by anymeans after initialization. (Hint use constant objects)
Create three static comparators: one that compares points based on their x, y, and distance from the origin; the other two compare points based on any Point2D data type. For the Point2D data type, create two non-static comparators: one that compares objects based on how far they are from a given point, and the other that compares objects based on how far they are from a given point polarly.
Create a data type that allows you to insert, remove the maximum, and delete the minimum (all in logarithmic time), as well as locate the maximum and find the minimum (both in constant time). Use two piles as a hint.
Input values seat1, travel_time1 and plane_num1 are read from input, representing the seat, travel_time, and plane_num of a passenger. An element representing a new seat is also read from input as new_seat. Complete the following tasks: Assign passenger1 with an instance of Passenger with seat1, travel_time1 and plane_num1 as attributes in that order. Call passenger1's print_data(). Call passenger1's move_seat() with argument new_seat. Call passenger1's print_data() again.
The code given below represents a saveTransaction() method which is used to save data to a database from the Java program. Given the classes in the image as well as an image of the screen which will call the function, modify the given code so that it loops through the items again, this time as it loops through you are to insert the data into the salesdetails table, note that the SalesNumber from the AUTO-INCREMENT field from above is to be inserted here with each record being placed into the salesdetails table. Finally, as you loop through the items the product table must be update because as products are sold the onhand field in the products table must be updated. When multiple tables are to be updated with related data, you should wrap it into a DMBS transaction. The schema for the database is also depicted. public class PosDAO { private Connection connection; public PosDAO(Connection connection) { this.connection = connection; } public void…
TODO 2 Complete the TODO by finding the locations of the data samples for each iris flower type. Hint: Use the code for how we found all the setosa_locs as an example! Using iris_df, find the location of all the data sample locations for the versicolor class which has a label of 1. To do so, use NumPy's np.where() function. Using iris_df, find the location of all the data sample locations for the virginica class which has a label of 2. To do so, use NumPy's np.where() function. setosa_locs = np.where(iris_df['class'] == 0)[0] # TODO 2.1versicolor_locs = # TODO 2.2virginica_locs = todo_check([ (type(versicolor_locs) is np.ndarray, "'versicolor_locs' is not type np.ndarray. Make sure you indexed np.where() currently!"), (type(virginica_locs) is np.ndarray, "'virginica_locs' is not type np.ndarray. Make sure you indexed np.where() currently!"), (versicolor_locs[0] == 50, "'versicolor_locs' has an incorrect value"), (virginica_locs[0] == 100, "'virginica_locs' has an…
Complete the docstring using the information attached (images):def upgrade_stations(threshold: int, num_bikes: int, stations: List["Station"]) -> int: """Modify each station in stations that has a capacity that is less than threshold by adding num_bikes to the capacity and bikes available counts. Modify each station at most once. Return the total number of bikes that were added to the bike share network. Precondition: num_bikes >= 0 >>> handout_copy = [HANDOUT_STATIONS[0][:], HANDOUT_STATIONS[1][:]] >>> upgrade_stations(25, 5, handout_copy) 5 >>> handout_copy[0] == HANDOUT_STATIONS[0] True >>> handout_copy[1] == [7001, 'Lower Jarvis St SMART / The Esplanade', \ 43.647992, -79.370907, 20, 10, 10] True """
Using explicit cursors write an anonymous block that displays the names of the shoppers followed by the names of the products that they have shopped. You will find this information in the bb_shopper(shopper name), bb_product (product name), bb_basket and bb_basketitem tables. It is fine if some of the product names are repeated in the output, this is because the shopper has brought an item more than once..