Create a table classifying the different algorithms

[hhummel]

Columns including binary classification, how hard to implement and estimate of quality

[gwaybio]

@dhimmel this sounds like a task to me :)

[yl565]

Some popular algorithms that has been already implemented in sklearn:

Type	Algorithm	Implementation	Quality
Discriminant Analysis	LDA/QDA	sklearn.discriminant_analysis
Generalized Linear Models	Logistic regression with L1/L2 regularization	sklearn.linear_model.LogisticRegressionCV
Generalized Linear Models	OLS	sklearn.linear_model.LinearRegression
Generalized Linear Models	Rridge	sklearn.linear_model.RridgeCV
Generalized Linear Models	LASSO	sklearn.linear_model.LassoCV
Generalized Linear Models	Elastic Net	sklearn.linear_model.ElasticNetCV
Robust	RANSAC	sklearn.linear_model.RANSACRegressor
Support Vector Machine	SVM	sklearn.svm.SVC (libsvm)
Support Vector Machine	Linear SVM with regularization	sklearn.svm.LinearSVC (liblinear)
Decision Trees	CART	sklearn.tree.DecisionTreeClassifier
Ensemble	Bagging/Random Subspace	sklearn.ensemble.BaggingClassifier
Ensemble	RandomForest	sklearn.ensemble.RandomForestClassifier
Ensemble	Adaboost	sklearn.ensemble.AdaBoostClassifier
Ensemble	Voting	sklearn.ensemble.VotingClassifier

Algorithms that need implementation

Type	Algorithm	Implementation	Quality
Ensemble	Stacking	Easy

[hhummel]

I went through the supervised learning classifiers in scikit-learn in the User's Guide, capturing the highlights of the blurb and url of the documentation.

Classifiers.xlsx

[hhummel]

I don't have a good enough feel for the problem we are solving or the characteristics of the data, but I like what they say about linear and quadratic discriminant analysis: "These classifiers are attractive because they have closed-form solutions that can be easily computed, are inherently multiclass, have proven to work well in practice and have no hyperparameters to tune."

[dhimmel]

I think we now have a good handle on what's available. Closing this issue, but feel free to continue discussion.