model

Explainability: Cracking open the black box, Part 1

By Manu Joseph, Problem Solver, Practitioner, Researcher at Thoucentric Analytics. Interpretability is the degree to which a human can understand…

linear, model, regression

How to Use Out-of-Fold Predictions in Machine Learning

Last Updated on December 6, 2019Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation. During the…

model, out-of-fold, predictions

Deep Learning Tutorial Demonstrates How to Simplify Distributed Deep Learning Model Inference Using Delta Lake and Apache Spark™

On October 10th, our team hosted a live webinar—Simple Distributed Deep Learning Model Inference—with Xiangrui Meng, Software Engineer at Databricks.…

deep, learning, model

Sniffing Out Errors

From this you gain an understanding of where your model is succeeding and what can be amended to improve performance.…

analysis, error, model

A Gentle Introduction to Model Selection for Machine Learning

Given easy-to-use machine learning libraries like scikit-learn and Keras, it is straightforward to fit many different machine learning models on…

different, model, selection

How to Save and Reuse Data Preparation Objects in Scikit-Learn

Last Updated on November 20, 2019It is critical that any data preparation performed on a training dataset is also performed…

data, dataset, model

How to Connect Model Input Data With Predictions for Machine Learning

Fitting a model to a training dataset is so easy today with libraries like scikit-learn. A model can be fit…

input, model, training

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

The paper is a mix of technical and philosophical arguments and comes with two main takeaways for me: firstly, a…

learning, machine, model

Raymond Willey

Predicting Heart Disease MortalityBuilding a machine learning model that can identify high-risk…Integrating Python & TableauWhen performing in-depth analyses on large and…

learning, machine, model

Benjamin Obi Tayo Ph.D.

Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data ScienceMedium Partner Program’s New Model for Calculating…

hyperparameters, model, program’s

9 Practical Actions to Improve Machine Learning for Fraud Prevention

Start with simpler, more transparent, and explainable and bias-free models and graduate to complicated models over time. MODELS – Experiment…

fraud, model, online

A Gentle Introduction to Expectation-Maximization (EM Algorithm)

Last Updated on November 1, 2019 Maximum likelihood estimation is an approach to density estimation for a dataset by searching…

likelihood, maximum, model

Probabilistic Model Selection with AIC, BIC, and MDL

Model selection is the problem of choosing one from among a set of candidate models. It is common to choose…

model, parameters, selection

Why is Machine Learning Deployment Hard?

By Alexandre Gonfalonieri, AI Consultant. After several AI projects, I realized that deploying Machine Learning (ML) models at scale is…

data, model, production

A Gentle Introduction to Logistic Regression With Maximum Likelihood Estimation

Last Updated on October 28, 2019 Logistic regression is a model for binary classification predictive modeling. The parameters of a…

logistic, model, regression

A Gentle Introduction to Linear Regression With Maximum Likelihood Estimation

Last Updated on October 25, 2019 Linear regression is a classical model for predicting a numerical quantity. The parameters of…

likelihood, maximum, model

Build your First Linear Regression Model in Qlik Sense

Think about it before you read the answer. The best line is the one that minimizes the distance of all…

linear, model, regression

A Gentle Introduction to Bayesian Belief Networks

Probabilistic models can define relationships between variables and be used to calculate probabilities. For example, fully conditional models may require…

model, random, variables