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Choosing the right machine learning algorithm
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| Adapted from [scikit learn cheat sheet](http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html). | |
| * More than 50 samples? | |
| * No | |
| * Get more data | |
| * Yes | |
| * Predicting a category? | |
| * No | |
| * Predicting a quantity? | |
| * No | |
| * Just looking? | |
| * No | |
| * Predicting structure? | |
| * Tough luck | |
| * Yes => DIMENSIONALITY REDUCTION | |
| * Randomized PCA | |
| * Not working? | |
| * Less than 10K samples? | |
| * No | |
| * Kernel approximation | |
| * Yes | |
| * Isomap | |
| * Spectral Embedding | |
| * Not working? | |
| * LLE | |
| * Yes => REGRESSION | |
| * Less than 100K samples? | |
| * No | |
| * SGD Regressor | |
| * Yes | |
| * Few features should be important? | |
| * No | |
| * RidgeRegression | |
| * SVR(Kernle='linear') | |
| * Not working? | |
| * SVR(Kernel='rbf') | |
| * EnsembleRegression | |
| * Yes | |
| * Lasso | |
| * ElasticNet | |
| * Yes | |
| * Labeled data? | |
| * No => CLUSTERING | |
| * Number of categories known? | |
| * No | |
| * Less than 10K samples? | |
| * No | |
| * Tough Luck | |
| * Yes | |
| * MeanShift | |
| * VBGMM | |
| * Yes | |
| * Less than 10K samlpes | |
| * No | |
| * MiniBatch KMeans | |
| * Yes | |
| * KMeans | |
| * Not working? | |
| * Spectral Clustering | |
| * GMM | |
| * Yes => CLASSIFICATION | |
| * Less than 100K samples? | |
| * No | |
| * SGD classifier | |
| * Not working? | |
| * Kernel approximation | |
| * Yes | |
| * Linear SVC | |
| * Not working? | |
| * Text data? | |
| * No | |
| * KNeighbors Classifier | |
| * Not working? | |
| * SVC | |
| * Ensemble classifiers | |
| * Yes | |
| * Naive Bayes |
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