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cognoma / machine-learning

Machine learning for Project Cognoma
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feature engineering #1

Closed autokad closed 7 years ago

autokad commented 8 years ago

• Feature Engineering o Feature Transformations  Log  Square  Inverse  Percentile  ZScore o Feature Creation  Interactions (+ * - )  LDA (Latent Dirichlet Allocation) • Feature Reduction (Selection / Extraction) o Stepwise Regression o RFE (Recursive Feature Elimination) o PCA (Principle Component Analysis) o LDA (Latent Dirichlet Allocation) o Linear Discriminate Analysis -Also LDA =/ o Genetic Algorithms o Wrapper Methods • Algorithms o Linear Regression  Ridge Regression  LASSO  Elastic Net  OLS (Ordinary Least Squares) o Logistic Regression o SVM (Support Vector Machine)  RBF Kernel (Radial Basis Function)  Polynomial & Linear Kernel  Histogram Kernel o Random Forest o Adaboost o Logitboost o KNN (K Nearest Neighbors) o Naïve Bayes o K Means o Perceptron o Neural Nets o GBM (Gradient Boosting Machines)

autokad commented 8 years ago

These can be broken down as functions we need to implement in django and also something we an all try to get a handle of machine learning, feel free to add to the list

yl565 commented 8 years ago
dhimmel commented 7 years ago

Closing this issue as part of a routine cleaning. People can still use this issue as a reference and keep discuss below. However, I don't think there are any unresolved action items related to this issue.