Cross validation

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This document contains a dataset with attributes describing weather conditions and whether a person decides to play or not. The attributes are outlook, temperature, humidity, windy, and play. There are 14 data points listed with values for each attribute. This dataset could be used for cross validation to test machine learning algorithms that aim to predict the "play" attribute based on the other weather attributes.

Data & Analytics

@relation weather
@attribute outlook {sunny, overcast, rainy}
@attribute temperature real
@attribute humidity real
@attribute windy {TRUE, FALSE}
@attribute play {yes, no}
@data
sunny,85,85,FALSE,no
sunny,80,90,TRUE,no
overcast,83,86,FALSE,yes
rainy,70,96,FALSE,yes
rainy,68,80,FALSE,yes
rainy,65,70,TRUE,no
overcast,64,65,TRUE,yes
sunny,72,95,FALSE,no
sunny,69,70,FALSE,yes
rainy,75,80,FALSE,yes
sunny,75,70,TRUE,yes
overcast,72,90,TRUE,yes
overcast,81,75,FALSE,yes
rainy,71,91,TRUE,no

This document discusses cross-validation techniques for evaluating machine learning models on a dataset and preventing overfitting. It introduces linear regression, quadratic regression, and join-the-dots/nonparametric regression on a sample regression problem. It then explains the test set method for model evaluation but notes its high variance. Leave-one-out cross-validation (LOOCV) and k-fold cross-validation are presented as alternatives that make more efficient use of data. Examples are given comparing the performance of different models using these cross-validation techniques on the sample regression problem. The document concludes by discussing how cross-validation can be used for model selection tasks like choosing the number of hidden units in a neural network or the k value in

User Guide of Regression Test & Validation Tool (v1.0)Chen Fang

This document provides a user guide for a regression test and validation tool. It introduces the tool's functions and operations for users and developers. For users, the guide describes the tool's graphical user interface and how to use it for different applications such as comparing simulation to CAN data or labeling CAN signals. It also provides setup instructions. For developers, it outlines the tool's software structure and parallel computing capabilities. Future improvements are discussed as well, such as adding vehicle image recognition.

Linear regressionansrivas21

Linear regression models a quantity as a linear combination of features to minimize residuals. Issues include unstable results from collinear features and overfitting. Regularization like ridge regression addresses this by minimizing residuals and coefficient size, improving variance. Cross-validation chooses the best model by evaluating prediction error on validation data rather than training error. Lasso induces sparsity by minimizing residuals and the L1 norm of coefficients. Scikit Learn implements these methods with options like normalization, intercept fitting, and solver choices.

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This document discusses heteroscedasticity, which occurs when the error variance is not constant. It provides examples of when the variance of errors may change, such as with income level or outliers. Graphical methods are presented for detecting heteroscedasticity by examining patterns in residual plots. Formal tests are also described, including the Park test which regresses the log of the squared residuals on explanatory variables, and the Glejser test which regresses the absolute value of residuals on variables related to the error variance. Detection of heteroscedasticity is important as it violates assumptions of the classical linear regression model.

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This document discusses heteroskedasticity in econometric models. It defines heteroskedasticity as non-constant variance of the error term, in contrast to the homoskedasticity assumption of constant variance. It explains that while OLS estimates remain unbiased with heteroskedasticity, the standard errors are biased. Robust standard errors can provide consistent standard errors even with heteroskedasticity. The Breusch-Pagan and White tests are presented as methods to test for the presence of heteroskedasticity based on the residuals. Weighted least squares is also introduced as a method to obtain more efficient estimates than OLS when the form of heteroskedasticity is known.

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Heteroscedasticity occurs when the variance of the error terms in a regression model are not constant, but instead vary depending on the values of the independent variables. While ordinary least squares estimators remain unbiased, their standard errors may be incorrect under heteroscedasticity. This means that confidence intervals and hypothesis tests based on the usual standard errors are unreliable and can lead to misleading conclusions.

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ChatGPT and the Future of Work - Clark Boyd Clark Boyd

The document provides an overview of generative AI, particularly focusing on ChatGPT and its implications for business and the job market. It discusses the rapid growth of generative AI, its applications in marketing, and the skills needed for the AI era while addressing potential job displacement and the need for adaptation. The agenda includes a Q&A session and emphasizes rethinking work in collaboration with AI technology.

Getting into the tech field. what next Tessa Mero

The document provides career advice for getting into the tech field, including: - Doing projects and internships in college to build a portfolio. - Learning about different roles and technologies through industry research. - Contributing to open source projects to build experience and network. - Developing a personal brand through a website and social media presence. - Networking through events, communities, and finding a mentor. - Practicing interviews through mock interviews and whiteboarding coding questions.

Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray

1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework. 2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections. 3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.

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5 Public speaking tips from TED - Visualized summarySpeakerHub

ChatGPT and the Future of Work - Clark Boyd Clark Boyd

Getting into the tech field. what next Tessa Mero

Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray

How to have difficult conversations Rajiv Jayarajah, MAppComm, ACC