Linear regression and logistic regression are covered by elastic net lasso, sgd regression, and sgd classifiers. Support vector machines are covered in SVCs and SVRs. I don't see neural networks on there (just saw this edit: "the chart is not really comprehensive, as I focused on scikit-learn. Otherwise I certainly would have included neural networks").
Some notes that may help clarify: regression is trying to predict a continuous number (e.g. how much a house will cost). Linear regression is a type of regression. Classification is trying to predict a category (e.g. condo vs apartment). Logistic regression is usually used for classification over two categories (even though it confusingly has 'regression' in the name). So linear regression will fall into into the regression bubble, logistic regression will fall into the classification bubble, and support vector machines are split into the two bubbles based on what you want to do with them.
The difference between sgd (stochastic gradient descent) and elastic net lasso is how the models are trained. The final model in both cases can be used (evaluated/predicted) in the same way.
As it says elsewhere, this diagram is only presenting algorithms as currently implemented in scikit-learn. Having said that, there is a place for a larger and more comprehensive chart/diagram. But again, as I said elsewhere, even when people ask for such things and promise to contribute, they don't, so I've stopped trying to make things like that.
Feel free to present an augmented version that include the things you mention. I, for one, would be interested in a more comprehensive diagram.
Comments
Maybe they're using different names, but where are: Linear Regression, Logistic Regression, Neural Networks, and SVMs?
Linear regression and logistic regression are covered by elastic net lasso, sgd regression, and sgd classifiers. Support vector machines are covered in SVCs and SVRs. I don't see neural networks on there (just saw this edit: "the chart is not really comprehensive, as I focused on scikit-learn. Otherwise I certainly would have included neural networks").
Some notes that may help clarify: regression is trying to predict a continuous number (e.g. how much a house will cost). Linear regression is a type of regression. Classification is trying to predict a category (e.g. condo vs apartment). Logistic regression is usually used for classification over two categories (even though it confusingly has 'regression' in the name). So linear regression will fall into into the regression bubble, logistic regression will fall into the classification bubble, and support vector machines are split into the two bubbles based on what you want to do with them.
The difference between sgd (stochastic gradient descent) and elastic net lasso is how the models are trained. The final model in both cases can be used (evaluated/predicted) in the same way.
As it says elsewhere, this diagram is only presenting algorithms as currently implemented in scikit-learn. Having said that, there is a place for a larger and more comprehensive chart/diagram. But again, as I said elsewhere, even when people ask for such things and promise to contribute, they don't, so I've stopped trying to make things like that.
Feel free to present an augmented version that include the things you mention. I, for one, would be interested in a more comprehensive diagram.