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Machine & Deep Learning Compendium
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The Machine & Deep Learning Compendium
Types Of Machine Learning
Data Science
Data Science Tools
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Data Science Management
Calculus
Probability & Statistics
Probability
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Features
Calibration
Multi Label Classification
Distribution
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Information Theory
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Normalization & Scaling
Regularization
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Meta Learning
Evaluation Metrics
Benchmarking
Hyper Parameter Optimization
Multi CPU Processing
Algorithms 101
Training Strategies
Classic Machine Learning
Label Algorithms
Clustering Algorithms
Anomaly Detection
Decision Trees
Active Learning Algorithms
Linear Separator Algorithms
Ensembles
Reinforcement Learning
Incremental Learning
Dimensionality Reduction Methods
Genetic Algorithms & Genetic Programming
Learning Classifier Systems
Recommender Systems
Timeseries
Fourier Transform
Digital Signal Processing (DSP)
Propensity Score Matching
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Experimental Design
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Label Algorithms

  1. 1.
    ​imbalance learn - is an open-source, MIT-licensed library that provides tools when dealing with classification with imbalanced classes.
  2. 2.
    ​Classifying Job Titles With Noisy Labels Using REINFORCE this article has a very nice trick in adding a reward component to the loss function in order to mitigate for unbalanced class label problem, instead of the usual balancing.
Imbalance Learn comparison

Note: very much related to weakly and semi supervision, i.e., we have small amounts of labels and we want to generalize the labels to other samples, see also weak supervision methods.
  1. 1.
    Step 1: build a laplacian graph using KNN, distance metric is minkowski with p=2, i.e. euclidean distance.
  2. 2.
    ​Step by step tutorial, part 2​
  3. 3.
    ​Spreading (propagation upgrade), Essentially a community graph algorithm, however it resembles KNN in its nature, using semi supervised data set (i.e., labeled and unlabeled data) to spread or propagate labels to unlabeled data, with small incrementations in the algorithm, using KNN-like methodology, each unlabeled sample will be given a label based on its 1st order friends, if there is a tie, a random label is chosen. Nodes are connected by using a euclidean distance.
  4. 4.
    ​Difference between propagation and spreading is a laplacian matrix, vs normalized LM
  5. 5.
    ​Laplacian matrix on youtube, videos 30-33​
  6. 6.
    ​Really good example notebook​
  7. 7.
    ​Spreading vs propagation​
  8. 8.
    ​https://en.wikipedia.org/wiki/Label_Propagation_Algorithm​
  9. 9.
    Youtube 1, 2, 3,
  10. 10.
    ​Medium,
  11. 11.
    ​Sklearn, 1, 2, 3, 4, 5,
  1. 1.
    ​Git, incremental LP​
  2. 2.
    ​Git2​
    1. 1.
      Harmonic Function (HMN) [Zhu+, ICML03]
    2. 2.
      Local and Global Consistency (LGC) [Zhou+, NIPS04]
    3. 3.
      Partially Absorbing Random Walk (PARW) [Wu+, NIPS12]
    4. 4.
      OMNI-Prop (OMNIProp) [Yamaguchi+, AAAI15]
    5. 5.
      Confidence-Aware Modulated Label Propagation (CAMLP) [Yamaguchi+, SDM16]
  3. 3.
    ​
  1. 1.
    Presentation 1,2 ​
Neo4j 1, 2, 3,

  1. 1.
    ​clean lab - "cleanlab is the data-centric ML ops package for machine learning with noisy labels. cleanlab cleans labels and supports finding, quantifying, and learning with label errors in datasets. See datasets cleaned with cleanlab at labelerrors.com. Check out the: cleanlab code documentation. cleanlab is powered by confident learning, published in this paper | blog."
    1. 1.
      Reference 1: Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks by Curtis G Northcutt, Anish Athalye, Jonas Mueller​
    2. 2.
      Reference 2: Confident Learning: Estimating Uncertainty in Dataset Labels​
    3. 3.
      can be used for positive unlabeled learning
  2. 2.
    ​PULearn - "Positive-unlabeled learning (aka PU-learning) is a machine learning scenario for binary classification where the training set consists of a set of positively-labeled examples and an additional unlabeled set that contains positive and negative examples in unknown proportions (so no training example is explicitly labeled as negative). Positive-unlabeled learning methods aim to incorporate the unique structure of this scenario into the learning process, in a way that improves generalization of the learned notion of the positive class, when compared to simply treating all unlabeled examples as negative examples, or alternatively discarding them and training a one-class classifier over only the positive samples."
  3. 3.
    ​PUMML, Medium - "Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to classify materials from only positive and unlabeled examples."
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Unbalanced labels
Label Propagation / Spreading
Label Noise