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Comment: Added set of questions for selecting RL Algorithm, also added tree chart


Volunteers


NameML Category
Jahanvi                                  Supervised                                        
AkankshaUnsupervised
Kanak RajReinforced

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Un-supervised

  1. Clustering -  hierarchical clustering, k-means, mixture models, DBSCAN, and OPTICS algorithm
  2. Anomaly Detection - Local Outlier Factor, and Isolation Forest
  3. Dimensionality Reduction - Principal component analysis, Independent component analysis, Non-negative matrix factorization, Singular value decomposition

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NameComments on ApplicabilityReference
Hierarchical Clustering
  1. (N-1) combination of clusters are formed to choose from.
  2. Expensive and slow. n×n  distance matrix needs to be made.
  3. Cannot work on very large datasets.
  4. Results are reproducible.
  5. Does not work well with hyper-spherical clusters.
  6. Can provide insights into the way the data pts. are clustered.
  7. Can use various linkage methods(apart from centroid).

k-means
  1. Pre-specified number of clusters.
  2. Less computationally intensive.
  3. Suited for large dataset.
  4. Point of start can be random which leads to a different result each time the algorithm runs.
  5. K-means needs circular data. Hyper-spherical clusters.
  6. K-Means simply divides data into mutually exclusive subsets without giving much insight into the process of division.
  7. K-Means uses median or mean to compute centroid for representing cluster.

Gaussian Mixture Models
  1. Pre-specified number of clusters.
  2. GMs are somewhat more flexible and with a covariance matrix we can make the boundaries elliptical (as opposed to K-means which makes circular boundaries).
  3. Another thing is that GMs is a probabilistic algorithm. By assigning the probabilities to data points, we can express how strong is our belief that a given data point belongs to a specific cluster.
  4. GMs usually tend to be slower than K-Means because it takes more iterations to reach the convergence. (The problem with GMs is that they have converged quickly to a local minimum that is not very optimal for this dataset. To avoid this issue, GMs are usually initialized with K-Means.)

Reinforcement Learning

  1. Active Learning
  2. No labeled data
  3. No supervisor, only  reward
  4. Actions are sequential
  5. Feedback is delayed, not instantaneous.
  6. Can afford to make mistakes?
  7. Is it possible to use a simulated environment for the task?
  8. Lots of time
  9. Think about the variables that can define the state of the environment.
    1. State Variables and Quantify them
    2. The agent has access to these variables at every time step
    3. Concrete Reward Function and Compute Reward after action
    4. Define Policy Function

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NameComments on ApplicabilityReference

Q Learning









Is RL Possible?

  1. Do you have very high computation power?
  2. Do you have lots of time to train an agent?
  3. Do you need your model to be self-explanatory, humans can understand the reasoning behind the predictions and decisions made by the model?
  4. Do you need your model to be easy to implement and maintain?
  5. Is it possible to try the problem several times and afford to make many mistakes?
  6. In your situation, do active and online learning of algorithms is possible i.e while learning by actions, explore new data space and then learn from such conditions and data?
  7. In your situation, Can the algorithm take sequential action and complete the task?
  8. Is it possible to define policy function, actions that the agent takes as a function of the agent's state and the environment.?
  9. Is it possible to define a function to receive feedback from actions, such that feedback helps to learn and take new action?
  10. Can you simulate an environment for the task so that algorithm can try lots of times and can make mistakes to learn?

Image Added