Archive | Python Machine Learning

Semi-Supervised Learning With Label Spreading

Semi-Supervised Learning With Label Spreading

Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data. A popular approach to semi-supervised learning is to create a graph that connects examples in the training dataset and propagates […]

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Box and Whisker Plots of L2 Penalty Configuration vs. Accuracy for Multinomial Logistic Regression

Multinomial Logistic Regression With Python

Multinomial logistic regression is an extension of logistic regression that adds native support for multi-class classification problems. Logistic regression, by default, is limited to two-class classification problems. Some extensions like one-vs-rest can allow logistic regression to be used for multi-class classification problems, although they require that the classification problem first be transformed into multiple binary […]

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Semi-Supervised Learning With Label Propagation

Semi-Supervised Learning With Label Propagation

Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data. A popular approach to semi-supervised learning is to create a graph that connects examples in the training dataset and propagate […]

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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 to Develop LARS Regression Models in Python

How to Develop LARS Regression Models in Python

Regression is a modeling task that involves predicting a numeric value given an input. Linear regression is the standard algorithm for regression that assumes a linear relationship between inputs and the target variable. An extension to linear regression involves adding penalties to the loss function during training that encourage simpler models that have smaller coefficient […]

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Nearest Shrunken Centroids With Python

Nearest Shrunken Centroids With Python

Nearest Centroids is a linear classification machine learning algorithm. It involves predicting a class label for new examples based on which class-based centroid the example is closest to from the training dataset. The Nearest Shrunken Centroids algorithm is an extension that involves shifting class-based centroids toward the centroid of the entire training dataset and removing […]

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How to Develop LASSO Regression Models in Python

How to Develop LASSO Regression Models in Python

Regression is a modeling task that involves predicting a numeric value given an input. Linear regression is the standard algorithm for regression that assumes a linear relationship between inputs and the target variable. An extension to linear regression invokes adding penalties to the loss function during training that encourages simpler models that have smaller coefficient […]

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