A | B | C | |
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1 | Filename | Demos featured in | Description |

2 | ml_generative_Gaussian | demo_multiclass_discrim, demo_multiclass_KDE, demo_multiclass_mix, demo_multiclass_NB, demo_multiclass_student | Computes the maximum likelihood Gaussian fit for the data. Targets y are expected to be empty and are ignored. The prediction function returns an [nTest, 1] vector with the likelihood of each data point in the test set. |

3 | ml_generative_KDE | demo_multiclass_discrim, demo_multiclass_KDE | Computes the maximum likelihood kernel density estimate fit for the data. Targets y are expected to be empty and are ignored. The prediction function returns an [nTest, 1] vector with the likelihood of each data point in the test set. |

4 | ml_generative_mixtureGaussian | demo_multiclass_discrim, demo_multiclass_mix | Finds the best fit of k Gaussians to the data using Expectation Maximization. Targets y are expected to be empty and are ignored. The prediction function returns an [nTest, 1] vector with the likelihood of each data point in the test set. |

5 | ml_generative_NB | demo_multiclass_discrim, demo_multiclass_NB | Computes the maximum likelihood Naive Bayes (Gaussian/categorical counts) fit for the data.Targets y are expected to be empty and are ignored. The prediction function returns an [nTest, 1] vector with the likelihood of each data point in the test set. |

6 | ml_generative_student | demo_multiclass_discrim, demo_multiclass_student | Computes the maximum likelihood Student-t fit for the data. Targets y are expected to be empty and are ignored. The prediction function returns an [nTest, 1] vector with the likelihood of each data point in the test set |

7 | ml_multiclass_1v1 | demo_multiclass_v1 | Train C(C-1)/2 binary classifiers for a C-way multiclass problem; each receives the samples of a pair of classes from the original training dataset, and votes on the most probable class |

8 | ml_multiclass_1vA | demo_multiclass_v1 | Train C binary classifiers on C classes |

9 | ml_multiclass_bagging | demo_multiclass_bagging | Classication based on the highest prediction among models trained on bootstrap samples of dataset. |

10 | ml_multiclass_basis | demo_multiclass_basis, demo_multiclass_multiclass_CV | Classification with a basis change. |

11 | ml_multiclass_boosting | demo_multiclass_boosting | Implements a SAMME AdaBoosting algorithm to boost a classifier (see in Zhu, Rosset, Zou, and Hastie 2006) |

12 | ml_multiclass_CV | demo_multiclass_multiclass_CV | This computes the "best" hyper-parameter(s) using cross-validation for classification and regression problems |

13 | ml_multiclass_decisionTree | demo_multiclass_decisions | A decision tree that classifies data into multiple class labels |

14 | ml_multiclass_ECOC | demo_multiclass_ECOC | Relabel classes into binary forms, and use combination of binary classification submodels to predict multiclass labels |

15 | ml_multiclass_GDA | demo_multiclass_discrim | Classification using a Gaussian generative model with shared full covariance matrices to fit each class |

16 | ml_multiclass_kernel_softmax | Fits a classification model by minimizing the kernel softmax loss function | |

17 | ml_multiclass_KNN | demo_multiclass_KNN | An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors. |

18 | ml_multiclass_logistic | demo_multiclass_bagging, demo_multiclass_basis, demo_multiclass_KNN, demo_multiclass_multiclass_CV | Classification using multinomial logistic regression |

19 | ml_multiclass_MLP | demo_multiclass_boosting | Classification using a multilayer perceptron with softmax loss |

20 | ml_multiclass_stump | demo_multiclass_decisions | Finds the best threshold across all features |

21 | ml_multiclass_SVM | demo_multiclass_SVM | Fits a linear classifier by maximizing the margin using SVM |

22 | ml_multiclass_CNN | demo_multiclass_CNN | Finds optimal set of parameters for simple convolutional neural network in multiclass classification regime. |