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Search results for "Machine Learning"

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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Probability Decision Surface for Logistic Regression on a Binary Classification Task

Plot a Decision Surface for Machine Learning Algorithms in Python

Classification algorithms learn how to assign class labels to examples, although their decisions can appear opaque. A popular diagnostic for understanding the decisions made by a classification algorithm is the decision surface. This is a plot that shows how a fit machine learning algorithm predicts a coarse grid across the input feature space. A decision […]

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Nested Cross-Validation for Machine Learning with Python

Nested Cross-Validation for Machine Learning with Python

The k-fold cross-validation procedure is used to estimate the performance of machine learning models when making predictions on data not used during training. This procedure can be used both when optimizing the hyperparameters of a model on a dataset, and when comparing and selecting a model for the dataset. When the same cross-validation procedure and […]

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