Statistical Learning


Neural Networks as a Corporation Chain of Command

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Neural networks are considered complicated and they are always explained using neurons and a brain function. Let us start with logistic regression. The logistic regression yields values form 0 to 1, and we can consider the process as making a evaluation. Note that this is how a logistic regression functions.


Accurately Measuring Model Prediction Error

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As model complexity increases (for instance by adding parameters terms in a linear regression) the model will always do a better job fitting the training data. Although the stock prices will decrease our training error (if very slightly), they conversely must also increase our prediction error on new data as they increase the variability of the model's predictions making new predictions worse. So we could get an intermediate level of complexity with a quadratic model like $Happiness a b\ Wealth c\ Wealth 2 \epsilon$ or a high-level of complexity with a higher-order polynomial like $Happiness a b\ Wealth c\ Wealth 2 d\ Wealth 3 e\ Wealth 4 f\ Wealth 5 g\ Wealth 6 \epsilon$. At these high levels of complexity, the additional complexity we are adding helps us fit our training data, but it causes the model to do a worse job of predicting new data.


Deducer Tutorial: Creating Linear Model using R Deducer Package

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Resdidual vs. Fitted: Shows the residuals of the model plotted against the predicted values. Therefore, the cooks distance helps the analysts to identify observations with Cook's values that are greater than 1. Leverage: Another plot to examine outliers and influence For models without interactions, component residual plots are given. Just like the term plots, added variable plots are used to examine the linearity of covariates.


SVM: The go-to method machine learning algorithm

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It works by classifying data through finding the line which separates data into classes.


Making data science accessible – Logistic Regression

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Logistic Regression is a specific approach for describing a binary outcome variable (for example yes/no). Examples where we've used Logistic Regression at Capital One: Logistic Regression is our trusty, faithful tool of choice for our core risk decisioning models. This allows us to identify the significant drivers as well as potential interaction terms to find the most effective. At a high level: Logistic Regression may help you with a binary prediction problem where you want high predictive accuracy but where the model algorithm can be easily implemented and understood.


Deep Learning with TensorFlow in Python

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Let's first learn about simple data curation practices, and familiarize ourselves with some of the data that are going to be used for deep learning using tensorflow. After preprocessing, let's peek a few samples from the training dataset and the next figure shows how it looks. Here is how the test dataset looks (a few samples chosen). Let's first train a simple LogisticRegression model from sklearn (using default parameters) on this data using 5000 training samples.


Career Alert, June 23

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Six Great Articles About Quantum Computing and HPC This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more. Six Great Articles About Quantum Computing and HPC This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more.


Data Piques Matrix Factorization in PyTorch

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You simply define your model for prediction, your loss function, and your optimization technique (Vanilla SGD, Adagrad, ADAM, etc...), and the computer will automatically calculate the gradient updates and optimize your model. We'll walk through the three steps to building a prototype: defining the model, defining the loss, and picking an optimization technique. The forward method will simply be our matrix factorization prediction which is the dot product between a user and item latent feature vector. In the language of neural networks, our user and item latent feature vectors are called embedding layers which are analogous to the typical two-dimensional matrices that make up the latent feature vectors.


Visualizing Time-Series Change

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To evaluate the different methods for visualizing change, I chose to examine population data from the three major North American countries. The chart above shows population of the United States, Mexico, Canada, and North America as a whole (including Central America and the Caribbean). While plotting change in absolute units allows us to make comparisons within specific datasets, it is not particularly effective for comparing change across datasets with vastly different scales. As a Managing Consultant, Data Science for FI Consulting, Nick creates data science solutions for financial institutions.


Vincent Granville

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The data was stored in an hierarchical database (digital images based on aerial pictures, the third dimension being elevation, and ground being segmented in different categories - water, crop, urban, forest etc.) The data was stored in an hierarchical database (digital images based on aerial pictures, the third dimension being elevation, and ground being segmented in different categories - water, crop, urban, forest etc.) Markov Chains Monte Carlo modeling (Bayesian hierarchical models appied to complex cluster structures) Spatio-tempral models Environmental statistics: storm modeling, extreme value theory, and assessing leaks at the Hanford nuclear reservation (Washington State), using spatio-temporal models applied to chromium levels measured in 12 wells. Environmental statistics: storm modeling, extreme value theory, and assessing leaks at the Hanford nuclear reservation (Washington State), using spatio-temporal models applied to chromium levels measured in 12 wells.