model

Gradient Boosting with Scikit-Learn, XGBoost, LightGBM, and CatBoost

Gradient boosting is a powerful ensemble machine learning algorithm. It’s popular for structured predictive modeling problems, such as classification and…

model, single, using

Can you Lie to your Deep Learning Model?

Can you fool your deep learning model? What does lying to your deep learning model even entail? This question we’re…

data, model, noise

How to Develop Multi-Output Regression Models with Python

Multioutput regression are regression problems that involve predicting two or more numerical values given an input example. An example might…

model, multioutput, regression

Chris

My Python Pandas Cheat SheetThe pandas functions I use every day as a data scientist and software…Scheduled Web Scraping with Django…

learning, machine, model

PyTorch Tutorial: How to Develop Deep Learning Models with Python

Predictive modeling with deep learning is a skill that modern developers need to know. PyTorch is the premier open-source deep…

deep, learning, model

Tim Book

Building A Custom Model in Scikit-LearnScikit-Learn is incredible. It allows its users to fit almost any machine learning model…Cleaner R…

incredible., model, scikit-learnscikit-learn

Software Interfaces for Machine Learning Deployment

By Luigi Patruno, Data Scientist and the founder of ML in Production. In our previous post on machine learning deployment, we introduced…

learning, machine, model

Imbalanced Classification with the Fraudulent Credit Card Transactions Dataset

Fraud is a major problem for credit card companies, both because of the large volume of transactions that are completed…

card, dataset, model

Imbalanced Classification with the Adult Income Dataset

Many binary classification tasks do not have an equal number of examples from each class, e. g. the class distribution…

classification, imbalanced, model

Predictive Model for the Phoneme Imbalanced Classification Dataset

Many binary classification tasks do not have an equal number of examples from each class, e. g. the class distribution…

class, dataset, model

Develop a Model for the Imbalanced Classification of Good and Bad Credit

Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks.…

dataset, model, variables

How to Develop an Imbalanced Classification Model to Detect Oil Spills

Many imbalanced classification tasks require a skillful model that predicts a crisp class label, where both classes are equally important.…

label, model, regression

How to Develop a Probabilistic Model of Breast Cancer Patient Survival

Developing a probabilistic model is challenging in general, although it is made more so when there is skew in the…

dataset, model, survival

Reality Bites: 3 Misconceptions that Can Lead to Microservice Mayhem

Microservices are core to organizations’ flexibility and agility in the digital world. But that doesn’t mean that microservices are right…

microservices, model, moving

A Gentle Introduction to Threshold-Moving for Imbalanced Classification

Classification predictive modeling typically involves predicting a class label. Nevertheless, many machine learning algorithms are capable of predicting a probability…

model, optimal, threshold

The Future of Machine Learning Will Include a Lot Less Engineering

By David LiCause, Data Scientist Building a useful machine learning product involves creating a multitude of engineering components, only a small…

building, model, training

How to Create a Test Set to Approximate Business Metrics Offline

Introduction Most Kaggle-like machine learning hackathons miss a core aspect of a machine learning workflow – preparing an offline evaluation…

model, offline, production

Data Validation for Machine Learning

Data is the sustenance that keeps machine learning going. No matter how powerful a machine learning and/or deep learning model…

learning, model, training

Build your first Machine Learning pipeline using scikit-learn!

Overview Understand the structure of a Machine Learning Pipeline Build an end-to-end ML pipeline on a real-world data Train a…

learning, machine, model

Jun M.

Understanding Regularization in Machine LearningHow to deal with overfitting using regularizationLogistic Regression from Scratch in RBuild a logistic regression model from matrix…

model, regression, regularizationlogistic

TensorFlow 2 Tutorial: Get Started in Deep Learning With tf.keras

Predictive modeling with deep learning is a skill that modern developers need to know. TensorFlow is the premier open-source deep…

deep, learning, model

Sergey Bryl

LTV prediction for a recurring subscription with RAnomaly Detection for Business Metrics with RThe larger and more complex the business the more…

attribution, model, multi-channel

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