Linear Discriminant Analysis is a linear classification machine learning algorithm. The algorithm involves developing a probabilistic model per class based on the specific distribution of ob... Read more

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.... Read more

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.... Read more

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... Read more

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... Read more

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... Read more

Machine 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... Read more

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... Read more

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... Read more

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. Auto-Sklearn is... Read more