Artificial Intelligence

Random forest

Classification or regression model that improves the accuracy of a simple decision tree by generating multiple decision trees and taking a majority vote of them to predict the output, which is a continuous variable (eg, age) for a regression problem and a discrete variable (eg, either black, white, or red) for classifcation

Business use cases

  • Predict call volume in call centers for staffing decisions
  • Predict power usage in an electrical-distribution grid


Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome

Business use cases

  • Detect fraudulent activity in credit-card transactions. Achieves lower accuracy than deep learning
  • Simple, low-cost way to classify images (eg, recognize land usage from satellite images for climate-change models). Achieves lower accuracy than deep learning

Gradient-boosting trees

Classification or regression technique that generates decision trees sequentially, where each tree focuses on correcting the errors coming from the previous tree model. The final output is a combination of the results from all trees

Business use cases

  • Forecast product demand and inventory levels
  • Predict the price of cars based on their characteristics (eg, age and mileage)

Simple neural network

Model in which artificial neurons (software-based calculators) make up an input layer, one or more hidden layers where calculations take place, and an output layer. It can be used to classify data or find the relationship between variables in regression problems.

Business use cases

  • Predict the probability that a patient joins a healthcare program
  • Predict whether registered users will be willing or not to pay a particular price for a product

Linear regression

Highly interpretable, standard method for modeling the past relationship between independent input variables and dependent output variables (which can have an infinite number of values) to help predict future values of the output variables

Business use cases

  • Understand product-sales drivers such as competition prices, distribution, advertisement, etc
  • Optimize price points and estimate product-price elasticities

Logistic regression

A model with some similarities to linear regression that's used for classification tasks, meaning the output variable is binary (eg, only black or white) rather than continuous (eg, an infinite list of potential colors)

Business use cases

  • Classify customers based on how likely they are to repay a loan
  • Predict if a skin lesion is benign or malignant based on its characteristics (size, shape, color, etc)

Linear/quadratic discriminant analysis

Upgrades a logistic regression to deal with nonlinear problems-those in which changes to the value of input variables do not result in proportional changes to the output variables

Business use cases

  • Predict client churn
  • Predict a sales lead's likelihood of closing

Decision tree

Highly interpretable classification or regression model that splits data-feature values into branches at decision nodes (eg, if a feature is a color, each possible color becomes a new branch) until a final decision output is made

Business use cases

  • Understand product attributes that make a product most likely to be purchased
  • Provide a decision framework for hiring new employees

Naive Bayes

Classification technique that applies Bayes theorem, which allows the probability of an event to be calculated based on knowledge of factors that might affect that event (eg, if an email contains the word "money," then the probability of it being spam is high

Business use cases

  • Analyze sentiment to assess product perception in the market
  • Create classifiers to filter spam emails

Support vector machine

A technique that's typically used for classification but can be transformed to perform regression. It draws a division between classes that's as wide as possible. It also can be generalized to solve nonlinear problems.

Business use cases

  • Predict how many patients a hospital will need to serve in a time period
  • Predict how likely someone is to click on an online ad

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