Machine Learning tasks

The two most common categories of tasks in Machine Learning are supervised learning and unsupervised learning.

  • task is a specific objective for our algorithms.
  • Algorithms can be swapped in and out, as long as we pick the right task.
  • In fact, one should always try multiple algorithms because we most likely won’t know which algorithm will perform best for our dataset.

Supervised Learning

Supervised learning includes tasks for “labeled” data (i.e. we have a target variable).

  • In practice, it’s often used as an advanced form of predictive modeling.
  • Each observation must be labeled with a “correct answer.”
  • Only then one can build a predictive model because one must tell the algorithm what’s “correct” while training it (hence, “supervising” it).
  • Regression is the task of modeling continuous target variables.
  • Classification is the task for modeling categorical (a.k.a. “class”) target variables.

Unsupervised Learning

Unsupervised learning includes tasks for “unlabeled” data (i.e. you do not have a target variable).

  • In practice, it’s often used either as a form of automated data analysis or automated signal extraction.
  • Unlabeled data has no predetermined “correct answer.”
  • You’ll allow the algorithm to directly learn patterns from the data (without “supervision”).
  • Clustering is the most common unsupervised learning task, and it’s for finding groups within your data.
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