Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
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TLDR
This work presents a way of thinking about machine learning that gives it its own place in the econometric toolbox, and aims to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble.Abstract:
Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the pre...read more
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Predicting neighborhoods' socioeconomic attributes using restaurant data.
Lei Dong,Carlo Ratti,Siqi Zheng +2 more
TL;DR: It is shown that an easily accessible and timely updated neighborhood attribute, restaurant, when combined with machine-learning models, can be used to effectively predict a range of socioeconomic attributes of urban neighborhoods.
Journal ArticleDOI
Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles
TL;DR: It is demonstrated how a novel synthesis of three methods — unsupervised topic modeling of text data to generate new measures of textual variance, sentiment analysis of textData, and supervised ML coding of facial images with a cutting-edge convolutional neural network algorithm — can shed light on questions related to CEO oral communication.
ReportDOI
Machine learning from schools about energy efficiency
TL;DR: In this article, the authors used high-frequency panel data on electricity consumption to study the effectiveness of energy efficiency upgrades in K-12 schools in California using a panel fixed effects approach.
Journal ArticleDOI
Machine learning in knee osteoarthritis: A review
Christos Kokkotis,Serafeim Moustakidis,Elpiniki I. Papageorgiou,Giannis Giakas,Dimitrios Tsaopoulos +4 more
TL;DR: Knee osteoarthritis is a big data problem in terms of data complexity, heterogeneity and size as it has been commonly considered in the literature and Machine Learning has attracted significant interest from the scientific community to cope with the aforementioned challenges.
Journal ArticleDOI
Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals
Jermain Kaminski,Christian Hopp +1 more
TL;DR: This article used a neural network and natural language processing approach to predict the outcome of crowdfunding startup pitches using text, speech, and video metadata in 20,188 crowdfunding campaigns and found that positive psychological language is salient in environments where objective information is scarce and where investment preferences are taste based.
References
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.
Journal ArticleDOI
Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak
TL;DR: In this article, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation.
Journal Article
On Model Selection Consistency of Lasso
Peng Zhao,Bin Yu +1 more
TL;DR: It is proved that a single condition, which is called the Irrepresentable Condition, is almost necessary and sufficient for Lasso to select the true model both in the classical fixed p setting and in the large p setting as the sample size n gets large.
Journal ArticleDOI
Clinical versus actuarial judgment
TL;DR: Research comparing these two approaches to decision-making shows the actuarial method to be superior, factors underlying the greater accuracy of actuarial methods, sources of resistance to the scientific findings, and the benefits of increased reliance on actuarial approaches are discussed.
Book
A Distribution-Free Theory of Nonparametric Regression
TL;DR: How to Construct Nonparametric Regression Estimates * Lower Bounds * Partitioning Estimates * Kernel Estimates * k-NN Estimates * Splitting the Sample * Cross Validation * Uniform Laws of Large Numbers