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## Illustrating Predictive Models with the ROC Curve

For example, we would all be furious if our email program’s spam classifier was only able to detect 50% of…

## Preventing Discriminatory Outcomes in Credit Models

Preventing Discriminatory Outcomes in Credit ModelsValeria CortezBlockedUnblockFollowFollowingJun 5Machine learning is being deployed to do large-scale decision making, which can strongly impact…

## No one knows where America’s helipads are, except this neural network

No one knows where America’s helipads are, except this neural networkGareth WalkerBlockedUnblockFollowFollowingJun 10Photo by Wayne Chan on UnsplashThe race to the futureI don’t…

## Boolean Logic Using the Scala Compiler

In this way, we check every single possible combination of boolean inputs to the function and whether the resulting expression…

## Beginning Python Programming — Part 3

Beginning Python Programming — Part 3Familiarizing ourselves with using operators and noneBob RoeblingBlockedUnblockFollowFollowingMay 20Photo by Antoine Dautry on UnsplashIf you are just stumbling onto this…

## Precision and Recall Trade-off and Multiple Hypothesis Testing

Measured chance probability of rejecting the " "null: %. 3f (should be %. 3f)" % (type_I_error_p, alpha))> 5000 out of…

## ROC Curves and the Efficient Frontier

In that case you catch every good investment but incur a significant cost. Also note that in the example ROC…

## Validating Type I and II Errors in A/B Tests in R

Validating Type I and II Errors in A/B Tests in R#ODSC – Open Data ScienceBlockedUnblockFollowFollowingMay 10In the below work, we will…

## Tracking N’Golo Kante, the Sung Hero of World Cup 2018

Tracking N’Golo Kante, the Sung Hero of World Cup 2018A quick dive on sports visualization with Python, Seaborn, and Matplotlibkevin rexis…

## Fake News Classification via Anomaly Detection

Fake News Classification via Anomaly DetectionCan we use anomaly detection to detect and filter fake news?Doron Shamia SadehBlockedUnblockFollowFollowingApr 30Fake news has…

## Entenda o que é AUC e ROC nos modelos de Machine Learning

Entenda o que é AUC e ROC nos modelos de Machine LearningVinícius RodriguesBlockedUnblockFollowFollowingOct 26, 2018As curvas AUC e ROC estão entre…

## Tokenizing Real Estate With Fi

Tokenizing Real Estate With FiBriceAldrichBlockedUnblockFollowFollowingApr 2In light of the news of Securitize and Elevated Returns tokenizing \$1B worth of real estate…

## Accuracy, Recall, Precision, F-Score & Specificity, which to optimize on?

Accuracy, Recall, Precision, F-Score & Specificity, which to optimize on?Based on your project, which performance metric to improve on?Salma GhoneimBlockedUnblockFollowFollowingApr 2I will…

## Data Cleaning with R and the Tidyverse: Detecting Missing Values

Data Cleaning with R and the Tidyverse: Detecting Missing ValuesJohn SullivanBlockedUnblockFollowFollowingMar 21Data cleaning is one of the most important aspects of…

## Predicting Subscribers on Youtube using Machine Learning

Predicting Subscribers on Youtube using Machine LearningNikola CiganovićBlockedUnblockFollowFollowingFeb 28IntroductionThis project is inspired by second chapter of the book “Hands on Machine…

## The good and the bad in the SpaceNet Off-Nadir Building Footprint Extraction Challenge

Let’s check three possible thresholds: 0. 25, 0. 5, and 0. 75:The fraction of ground truth footprints correctly identified (purple),…

## The Ethical Matrix of Dzud Forecasting

This is what we try to assess when looking at false positives and false negatives. A false positive in this…

## Taming False Discoveries with Empirical Bayes

You probably read something written by a geographer who relied on centuries of observations by people and perhaps even artifacts…

## Grid Search for model tuning

Now that we have the baseline accuracy, let’s build a Logistic regression model with default parameters and evaluate the model.Output :By fitting…

## Getting Data ready for modelling: Feature engineering, Feature Selection, Dimension Reduction (Part two)

It then ranks the features based on the order of their elimination.# Recursive Feature Eliminationfrom sklearn.feature_selection import RFEfrom sklearn.linear_model import…

## A Guide to Machine Learning in R for Beginners: Logistic Regression

In the middle, around (0.3, 0.8), we’re correctly labeling about 80% of the poor care cases, with a 30% false…