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Search results for "Machine Learning"

Blending Ensemble Machine Learning With Python

Blending Ensemble Machine Learning With Python

Blending is an ensemble machine learning algorithm. It is a colloquial name for stacked generalization or stacking ensemble where instead of fitting the meta-model on out-of-fold predictions made by the base model, it is fit on predictions made on a holdout dataset. Blending was used to describe stacking models that combined many hundreds of predictive […]

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Line Plot of Decision Tree Accuracy on Train and Test Datasets for Different Tree Depths

How to Identify Overfitting Machine Learning Models in Scikit-Learn

Overfitting is a common explanation for the poor performance of a predictive model. An analysis of learning dynamics can help to identify whether a model has overfit the training dataset and may suggest an alternate configuration to use that could result in better predictive performance. Performing an analysis of learning dynamics is straightforward for algorithms […]

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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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