Search results for "Machine Learning"

Line Plot of Accuracy vs. Hill Climb Optimization Iteration for the Diabetes Dataset

How to Hill Climb the Test Set for Machine Learning

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 platforms impose limitations to prevent it, which is important. Nevertheless, […]

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Line Plot of Number of Cores Used During Training vs. Execution Speed

Multi-Core Machine Learning in Python With Scikit-Learn

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 models using resampling procedures like k-fold cross-validation, and tuning model hyperparameters, such as […]

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Automated Machine Learning (AutoML) Libraries for Python

Automated Machine Learning (AutoML) Libraries for Python

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 task. Open-source libraries are available for using AutoML methods with popular machine […]

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HyperOpt for Automated Machine Learning With Scikit-Learn

HyperOpt for Automated Machine Learning With Scikit-Learn

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 supports AutoML with HyperOpt for the popular Scikit-Learn machine learning library, including the suite of data preparation […]

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TPOT for Automated Machine Learning in Python

TPOT for Automated Machine Learning in Python

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 learning library for data transforms and machine learning algorithms and uses a Genetic Programming stochastic global […]

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Auto-Sklearn for Automated Machine Learning in Python

Auto-Sklearn for Automated Machine Learning in Python

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 an open-source library for performing AutoML in Python. It makes use of the popular Scikit-Learn machine learning library for data transforms and machine learning algorithms and uses a Bayesian Optimization search procedure […]

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Scikit-Optimize for Hyperparameter Tuning in Machine Learning

Scikit-Optimize for Hyperparameter Tuning in Machine Learning

Hyperparameter 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, although modern methods, such as Bayesian Optimization, are fast and effective. The Scikit-Optimize library is an […]

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What are X and y in machine learning?

Machine learning algorithms learn how to map examples of input to examples of output. This is useful because in the future we can give new examples of input and the model can predict the output. Therefore, when we train a model, we must separate our data (rows) into input and output elements (columns) Input is referred […]

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