Supervised vs. Unsupervised Learning

Supervised Problems & Learning

  • In supervised problems, specific purpose or target has been specified

    • By supervised problems, for example, a teacher “supervises” the learner by carefully providing target information along with a set of examples.
  • Supervised problem examples

    “Can we find groups of customers who have particularly high likelihoods of canceling their service soon after their contracts expire?”

    “Identify customers who will leave when her contract expires”

  • Supervised Learning

    • Machine learning task of inferring a function from labeled training data

Supervised Learning Techniques

  • A specific purpose for the grouping or predicting the target

  • the results often are much more useful

  • Examples

    • Classification
    • Regression
    • Causal modeling
    • Similarity matching
    • Link prediction
    • Data reduction
  • Two Main Supervised Techniques

    • Classification

      • Type of target – categorical (often binary) target

        “Will this customer purchase service S1 if given incentive I?”

        “Which service package (S1, S2, or none) will a customer likely purchase if given incentive I?”

      • [Note] class probability estimation

        • a numerical prediction over a categorical target
        • In the churn example, a basic yes/no prediction of whether a customer is likely to continue to subscribe to the service may not be sufficient
        • we want to model the probability that the customer will continue.
    • Regression

      • Type of target – numeric target

        “How much will this customer use the service?”

Unsupervised Problems & Learning

  • Learners would be given no information about the purpose of the learning, but would be left to form its own conclusions

  • no specific purpose or target has been specified

  • Problem example

    “Do our customers naturally fall into different groups?”

Unsupervised Learning Techniques

  • They usually produce groupings based on similarities, but there is no guarantee that these similarities are meaningful or will be useful for any particular purpose

  • Examples

    • Clustering
    • Co-occurrence grouping
    • Profiling

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