wehub-resource-sync
21 lines
960 B
TeX
21 lines
960 B
TeX
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
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