Publications by Richie R.
DATA 624 - Homework 9
Some of these models take a long time to train so we’ll use doParallel to make it faster library(doParallel) cluster <- makeCluster( detectCores() - 1 ) registerDoParallel( cluster ) 8.1 Recreate the simulated data from Exercise 7.2: library(mlbench) set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(sim...
5530 sym R (6710 sym/55 pcs) 3 img
DATA 624 - Homework 7
Exercise 7.2 Friedman (1991) introduced several benchmark data sets create by simulation. One of these simulations used the following nonlinear equation to create data: \[y = 10sin(πx_1x_2) + 20(x_3 - 0.5)^2 + 10x_4 + 5x_5 + N(0, \sigma^2)\] where the x values are random variables uniformly distributed between [0, 1] (there are also 5 other no...
3936 sym R (11042 sym/60 pcs) 14 img
DATA 624 - Homework 7
Question 6.2 6.2. Developing a model to predict permeability (see Sect. 1.4) could save significant resources for a pharmaceutical company, while at the same time more rapidly identifying molecules that have a sufficient permeability to become a drug: (a) Start R and use these commands to load the data: library(AppliedPredictiveModeling) data(...
5189 sym R (40521 sym/51 pcs) 6 img
DATA 624 - Project 1: Power Forecast
From the assignment description: Part B consists of a simple dataset of residential power usage for January 1998 until December 2013. Your assignment is to model these data and a monthly forecast for 2014. The data is given in a single file. The variable ‘KWH’ is power consumption in Kilowatt hours, the rest is straight forward. 1. Loadin...
4600 sym R (6162 sym/36 pcs) 8 img
DATA 624 - Homework 6
Question 9.1 Explain the differences among these figures. Do they all indicate that the data are white noise? Each of these graphs show the same y scale. We can use that to notice that the spread is smaller in each subsequent chart. The blue lines are fixed values where the autocorrelations would be statistically significantly different from...
9478 sym Python (4782 sym/35 pcs) 35 img
DATA 624 - Homework 5
Question 8.1 Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. Use the ETS() function to estimate the equivalent model for simple exponential smoothing. Find the optimal values of \(α\) and \(ℓ_0\), and generate forecasts for the next four months. library(fpp3) ## Warning: package 'fpp3' was bu...
8140 sym R (8896 sym/65 pcs) 17 img
DATA 624 - Homework 4
These questions come from the Applied Predictive Modeling book. Question 3.1 The UC Irvine Machine Learning Repository6 contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements:...
6253 sym R (5724 sym/44 pcs) 5 img
DATA 624 - Homework 3
# Importing the library library("fpp3") Question 5.1 Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appropriate in each case: A. Australian Population (global_economy) # Getting the global_economy data data(global_economy) head(global_economy) # Filter to Australian population a...
7239 sym R (13718 sym/74 pcs) 21 img
DATA624 Homework 2
Question 3.1 Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time? # Importing the library library("fpp3") ## Warning: package 'fpp3' was built under R version 4.3.3 ## Registered S3 method overwritten by 'tsibble': ## ...
7934 sym R (7216 sym/58 pcs) 29 img
DATA624 Homework 1
Question 2.1 Explore the following four time series: Bricks from aus_production, Lynx from pelt, Close from gafa_stock, Demand from vic_elec. # Loading the datasets data(aus_production) data(pelt) data(gafa_stock) data(vic_elec) A: Use ? (or help()) to find out about the data in each series. ?aus_production ?pelt ?gafa_stock ?vic_elec B: ...
5332 sym R (9060 sym/61 pcs) 23 img 1 tbl