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Top 10 Data Science Practioner Pitfalls - Mark Landry

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7. logistics regression using spss

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This document provides guidance on performing and interpreting logistic regression analyses in SPSS. It discusses selecting appropriate statistical tests based on variable types and study objectives. It covers assumptions of logistic regression like linear relationships between predictors and the logit of the outcome. It also explains maximum likelihood estimation, interpreting coefficients, and evaluating model fit and accuracy. Guidelines are provided on reporting logistic regression results from SPSS outputs.

08 neural networks

ankit_ppt

This document provides legal notices and disclaimers for an informational presentation by Intel. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that Intel technologies' features and benefits depend on system configuration. Finally, it specifies that the sample source code in the presentation is released under the Intel Sample Source Code License Agreement and that Intel and its logo are trademarks.

Your Classifier is Secretly an Energy based model and you should treat it lik...

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The document discusses research on learning to improve the efficiency of machine learning algorithms through speedup learning. It provides three key points: 1) Early work on explanation-based learning for speedup had limited success, but techniques like memoization and clause learning led to major improvements in SAT solvers. 2) More recent approaches use machine learning to build predictive models of problem instances and solver behavior, in order to inform strategies like automatic noise setting and randomized restart policies. 3) Case studies demonstrate these learning-based approaches can outperform traditional techniques and fixed policies by customizing resource allocation and reformulation based on problem structure and solver progress.

20150603 establishing theoretical minimal sets of mutants (Lab seminar)

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This document presents a formal model for determining theoretical minimal sets of mutants for a given test set. It defines concepts like redundant mutants, minimal test sets, and minimal mutant sets. An approach called dynamic subsumption is suggested to efficiently compute minimal mutant sets by identifying redundant mutants based on test killing behavior. An assessment on benchmark programs shows that most mutants are redundant, minimal test sets are small, and reduced mutation approaches generate many more mutants than truly necessary according to the minimal sets.

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The document discusses different techniques for cross-validation in machine learning. It defines cross-validation as a technique for validating model efficiency by training on a subset of data and testing on an unseen subset. It then describes various cross-validation methods like hold out validation, k-fold cross-validation, leave one out cross-validation, and their implementation in scikit-learn.

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Dhritiman Chakrabarti

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Greg Landrum

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This document provides an overview of resampling techniques including the bootstrap, permutation tests, and parametric bootstrap. It discusses how these methods can be used to estimate variances and confidence intervals for statistics. It also covers how the bootstrap can be used for hypothesis testing and improving predictions through techniques like bagging. Examples are provided for implementing various resampling methods in JMP using JSL scripts.

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Unsupervised generative methods have undergone a recent renaissance, spurred on in large part by impressive photo-realistic results in image applications. These generative methods seek to yield models that understand data by learning how to generate samples through implicit and explicit likelihood optimization. However, despite the surge in interest, these models are limited in several key aspects. First, although methods with an explicit likelihood are, in principle, able to perform additional tasks like anomaly detection and imputation, biases in the learned likelihood render these models useless for such important tasks. For example, recent work has shown that modern methods lead to high out-of-distribution likelihoods for data that is unlike seen training instances. Secondly, most current generative methods are limited to fixed-length vector or sequential data, leaving a substantial gap for the analysis of exchangeable data like sets and graphs. I.e., modern generative models excel at modeling dependencies among features in a point, but are lacking in modeling dependencies amongpoints in a collection. In this talk I discuss these shortcomings and suggest some possible avenues for improvement.

Performance Measurement for Machine Leaning.pptx

toneve4907

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Machine learning can be used to predict whether a user will purchase a book on an online book store. Features about the user, book, and user-book interactions can be generated and used in a machine learning model. A multi-stage modeling approach could first predict if a user will view a book, and then predict if they will purchase it, with the predicted view probability as an additional feature. Decision trees, logistic regression, or other classification algorithms could be used to build models at each stage. This approach aims to leverage user data to provide personalized book recommendations.

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Similar to 기조강연3: 실험설계의 강화 - 단순함에서 정교함으로 (JMP Ryan Lekivetz & Elizabeth Claassen) (20)

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Lecture on AI and Machine Learning

Linear Probability Models and Big Data: Prediction, Inference and Selection Bias

6 Evaluating Predictive Performance and ensemble.pptx

7. logistics regression using spss

08 neural networks

Your Classifier is Secretly an Energy based model and you should treat it lik...

Learning to Search Henry Kautz

20150603 establishing theoretical minimal sets of mutants (Lab seminar)

crossvalidation.pptx

Inferential statistics nominal data

Machine learning session6(decision trees random forrest)

Building useful models for imbalanced datasets (without resampling)

The Bootstrap and Beyond: Using JSL for Resampling

Introduction to Modeling

H2O World - Top 10 Data Science Pitfalls - Mark Landry

Deep Learning Opening Workshop - Improving Generative Models - Junier Oliva, ...

Performance Measurement for Machine Leaning.pptx

Intro to ml_2021

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