rasbt--python-machine-learning-book
wehub-resource-sync
27 行
1.1 KiB
Markdown
27 行
1.1 KiB
Markdown
Sebastian Raschka, 2015
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Python Machine Learning - Code Examples
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## Chapter 3 - A Tour of Machine Learning Classifiers Using Scikit-learn
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- Choosing a classification algorithm
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- First steps with scikit-learn
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- Training a perceptron via scikit-learn
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- Modeling class probabilities via logistic regression
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- Logistic regression intuition and conditional probabilities
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- Learning the weights of the logistic cost function
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- Training a logistic regression model with scikit-learn
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- Tackling overfitting via regularization
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- Maximum margin classification with support vector machines
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- Maximum margin intuition
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- Dealing with the nonlinearly separable case using slack variables
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- Alternative implementations in scikit-learn
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- Solving nonlinear problems using a kernel SVM
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- Using the kernel trick to find separating hyperplanes in higher dimensional space
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- Decision tree learning
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- Maximizing information gain – getting the most bang for the buck
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- Building a decision tree
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- Combining weak to strong learners via random forests
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- K-nearest neighbors – a lazy learning algorithm
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- Summary |