How to Hill Climb the Test Set for Machine Learning

Tweet Share Share Hill climbing the test set is an approach to achieving good or perfect predictions on a machine learning competition without touching the training set or even developing a predictive model. As an approach to machine learning competitions, it is rightfully frowned upon, and most competition...

1d

How to Train to the Test Set in Machine Learning

Tweet Share Share Training to the test set is a type of overfitting where a model is prepared that intentionally achieves good performance on a given test set at the expense of increased generalization error. It is a type of overfitting that is common in machine learning competitions where a complete...

3d

Multi-Core Machine Learning in Python With Scikit-Learn

Tweet Share Share Many computationally expensive tasks for machine learning can be made parallel by splitting the work across multiple CPU cores, referred to as multi-core processing. Common machine learning tasks that can be made parallel include training models like ensembles of decision trees, evaluating...

5d

Automated Machine Learning (AutoML) Libraries for Python

Tweet Share Share AutoML provides tools to automatically discover good machine learning model pipelines for a dataset with very little user intervention. It is ideal for domain experts new to machine learning or machine learning practitioners looking to get good results quickly for a predictive modeling...

2w

Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)

Tweet Share Share Machine learning model selection and configuration may be the biggest challenge in applied machine learning. Controlled experiments must be performed in order to discover what works best for a given classification or regression predictive modeling task. This can feel overwhelming...

2w

Hyperparameter Optimization With Random Search and Grid Search

Tweet Share Share Last Updated on September 19, 2020Machine learning models have hyperparameters that you must set in order to customize the model to your dataset. Often the general effects of hyperparameters on a model are known, but how to best set a hyperparameter and combinations of interacting...

2w

HyperOpt for Automated Machine Learning With Scikit-Learn

Tweet Share Share Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. HyperOpt is an open-source library for large scale AutoML and HyperOpt-Sklearn is a wrapper for HyperOpt that...

3w

TPOT for Automated Machine Learning in Python

Tweet Share Share Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. TPOT is an open-source library for performing AutoML in Python. It makes use of the popular Scikit-Learn machine...

3w

Auto-Sklearn for Automated Machine Learning in Python

Tweet Share Share Last Updated on September 12, 2020Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. Auto-Sklearn is an open-source library for performing AutoML in Python....

3w

Scikit-Optimize for Hyperparameter Tuning in Machine Learning

Tweet Share Share Last Updated on September 7, 2020Hyperparameter optimization refers to performing a search in order to discover the set of specific model configuration arguments that result in the best performance of the model on a specific dataset. There are many ways to perform hyperparameter optimization,...

4w