Search results for "Machine Learning"

How do I apply machine learning to my domain or industry?

This is an open question, but I have some ideas. 1) Perhaps you can formulate an existing problem from your industry as a supervised learning problem and see if machine learning algorithms can perform well or better than other methods. This framework may help: How to Define Your Machine Learning Problem 2) Perhaps you can […]

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Box and Whisker Plots of Bits Per Class vs. Distribution of Classification Accuracy for ECOC

Error-Correcting Output Codes (ECOC) for Machine Learning

Machine learning algorithms, like logistic regression and support vector machines, are designed for two-class (binary) classification problems. As such, these algorithms must either be modified for multi-class (more than two) classification problems or not used at all. The Error-Correcting Output Codes method is a technique that allows a multi-class classification problem to be reframed as […]

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Line of Best Fit for Huber Regression on a Dataset with Outliers

Robust Regression for Machine Learning in Python

Regression is a modeling task that involves predicting a numerical value given an input. Algorithms used for regression tasks are also referred to as “regression” algorithms, with the most widely known and perhaps most successful being linear regression. Linear regression fits a line or hyperplane that best describes the linear relationship between inputs and the […]

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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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