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tito-kimbo edited this page Aug 6, 2018 · 8 revisions

#What is Machine Learning?

Machine Learning (ML) is a field of Computer Science that allows machines to make predictions about certain features of data from previously observed data. BMLF offers access to 3 basic ML models: Classification, Regression, and Clustering. In addition, it offers data reduction capabilities for improved efficiency.

Classification

Classification is the problem of identifying to which category of a given set a new observation belongs. BMLF offers several classification models listed below, which perform better in specific use cases.

  1. SVM: General purpose, very useful with the appropriate tweaks.
  2. Multi-Layer Perceptron (MLP): May perform better than the other algorithms on sufficiently large datasets. It is sensitive to feature scaling and data reduction.
  3. Gaussian Naive-Bayes (Gaussian NB): Intended for use on continuous (real-valued) data.
  4. Multinomial Naive-Bayes (Multinomnial NB): Intended for use when dataset consists of 'counts'.
  5. Bernoulli Naive-Bayes (Bernoulli NB): Intender for use when it matters whether an event has occurred or not (for example on boolean data).

Regression

Regression or regression analysis is a set of statistical processes for estimating the relationships between variables. BMLF also offers several regression models, which are listed below with their intended use cases.

  1. Linear
  2. Elasticnet:
  3. ElasticnetCV:
  4. Bayes Ridge:
  5. Orthogonal:
  6. OrthogonalCV:
  7. Theil:
  8. SGD:
  9. Perceptron:
  10. Passive-agressive:

Clustering

Clustering or cluster analysis solves the problem of grouping items in different sets such that objects in the same set are more similar (in a certain sense) between them than when compared to items from other sets. BMLF offers basic clustering functionality.

  1. KMeans:
  2. Affinity:
  3. Mean Shift:
  4. Agglomerative:
  5. DBScan:
  6. Birch:

Reduction

Data reduction consists of a series of techniques that allow a user to reduce the amount of data and/or features available to make ML models more efficient while minimizing information loss. Currently, BMLF only supports Principal Component Analysis (PCA) which reduces the amount of features and transforms linearly correlated data into a set of linearly incorrelated variables. The supported PCA models are

  1. Automatic PCA:
  2. Incremental PCA:
  3. Kernel PCA:

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