Search results for "Machine Learning"

Algorithms for Optimization

3 Books on Optimization for Machine Learning

Optimization is a field of mathematics concerned with finding a good or best solution among many candidates. It is an important foundational topic required in machine learning as most machine learning algorithms are fit on historical data using an optimization algorithm. Additionally, broader problems, such as model selection and hyperparameter tuning, can also be framed […]

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What Is Meta-Learning in Machine Learning?

What Is Meta-Learning in Machine Learning?

Meta-learning in machine learning refers to learning algorithms that learn from other learning algorithms. Most commonly, this means the use of machine learning algorithms that learn how to best combine the predictions from other machine learning algorithms in the field of ensemble learning. Nevertheless, meta-learning might also refer to the manual process of model selecting […]

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Calculus Books for Machine Learning

Calculus Books for Machine Learning

Knowledge of calculus is not required to get results and solve problems in machine learning or deep learning. However, knowing some calculus will help you in a number of ways, such as in reading mathematical notation in books and papers, and in understanding the terms used to describe fitting models like “gradient,” and in understanding […]

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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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