Publications by Mircea Dumitru
Modeling Data in the Tidyverse
1 Reading and Cleaning the Data library(tidyverse) library(tidymodels) library(here) library(janitor) train <- read_csv(here::here("data", "tidy_data", "data_complaints_train.csv")) library(skimr) #glimpse(train) #library(visdat) #vis_dat(train) #vis_miss(train) skim(train) Table 1.1: Data summary Name train Number of rows 90975 Number of co...
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Practical Machine Learning: Quiz 4
Question 1. Load the vowe.train and vowel.test data sets library(ElemStatLearn) data(vowel.train) data(vowel.test) Set the variable y to be a factor variable in both the training and test set. Then set the seed to 33833. Fit * a random forest predictor relating the factor variable \(y\) to the remaining variables * a boosted predictor using ‘gbm�...
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Practical Machine Learning: Week 4
Regularized Regression Basic Idea Fit a regression model Penalize (or shrink) large coefficients Pros: Can help with the bias/variance tradeoff Can help with model selection Cons: May be computionally demanding on large data sets Does not perform as well as random forests or boosting A motivating example Suppose a regression model with two...
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Practical Machine Learning: Quiz 3
Question 1 Load the cell segmentation data from the AppliedPredictiveModeling package using the commands: library(AppliedPredictiveModeling) data(segmentationOriginal) suppressMessages(library(caret)) Subset the data to a training set and testing set based on the Case variable in the data set. Set the seed to 125 and fit a CART model with the rpar...
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Practical Machine Learning: Week 3
Predicting with trees Key ideas Iteratively split variables into groups. Evaluate homogeneity within each group. Split again if necessary. Pros Easy to interpret. Better performances in non-linear settings. Cons Without prunning/cross-validation can lead to overfitting. Harder to estimate uncertainty. Results may be variable, depending the ex...
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Practical Machine Learning: Quiz 2
Question 1 Load the Alzheimer’s disease data using the commands: library(AppliedPredictiveModeling) data(AlzheimerDisease) Which of the following commands will create non-overlapping training and test sets with about 50% of the observations assigned to each? Answer 1 library(caret) ## Loading required package: ggplot2 ## Loading required package...
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Practical Machine Learning: Week 2
The caret package The caret package (short for Classification And REgression Training) is a front end package that wraps around a lot of the prediction algorithms and tools in the R programming language. https://topepo.github.io/caret/ The package contains tools for: Preprocessing (cleaning) preProcess Data splitting createDataPartition create...
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Regression Models: Generalized Linear Models - Week 4
Module 11: GLMs The three most famous cases of GLMs are: * linear models, * binomial and binary regression * Poisson regression. Linear models Linear models are the most useful applied statistical technique. However, they are not without their limitations. The assumption of an additive response model is not justified if the response is discrete o...
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Regression Models: Generalized Linear Models - Quiz 4
Question 1 Consider the space shuttle data ?shuttle in the MASS library. Consider modeling the use of the autolander as the outcome (variable name use). Fit a logistic regression model with autolander (variable auto) use (labeled as “auto” 1) versus not (0) as predicted by wind sign (variable wind). Give the estimated odds ratio for autolander ...
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Regression Models: Assignment
Executive Summary Looking at the data set of a collection of cars mtcars, we are interested in exploring the relationship between a set of variables and miles per gallon (MPG) (outcome). The main questions addressed are: Is an automatic or manual transmission better for MPG? Quantify the MPG difference between automatic and manual transmissions? ...
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