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Definition

Supervised Learning

• Linear regression is a method for finding the straight line or hyperplane that best fits a set of points.

• Logistic Regression is a powerful statistical way of modeling a binomial outcome with one or more explanatory variables. It measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution. Examples:

  • Credit Scoring
  • Measuring the success rates of marketing campaigns
  • Predicting the revenues of a certain product
  • Is there going to be an earthquake on a particular day?

• Naive Bayes is based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable. Examples:

  • To mark an email as spam or not spam
  • Classify a news article about technology, politics, or sports
  • Check a piece of text expressing positive emotions, or negative emotions?
  • Used for face recognition software

• Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side. Example:

  • Finding mileage
  • Document classification
  • Image classification

• Decision Trees are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. Example:

  • Automobile price prediction (Gradient Boosting Regression - which uses several weak decision trees)

• Random Forest is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Example:

  • Predicting Diabetes

Unsupervised Learning

• k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.

• Density-based spatial clustering of applications with noise (DBSCAN) It is a density-based clustering non-parametric algorithm; given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away).

• Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components

These are a versatile algorithms that can be used for any type of grouping. Some examples of use cases are:

• Behavioral segmentation:
  • Segment by purchase history
  • Segment by activities on application, website, or platform
  • Define personas based on interests
  • Create profiles based on activity monitoring
• Inventory categorization:
  • Group inventory by sales activity
  • Group inventory by manufacturing metrics
• Sorting sensor measurements:
  • Detect activity types in motion sensors
  • Group images
  • Separate audio
  • Identify groups in health monitoring
• Detecting bots or anomalies:
  • Separate valid activity groups from bots
  • Group valid activity to clean up outlier detection

Something Else

• Reinforcement Learning, where we try to create a model that learns the rules of an environment to best maximize its return or reward.