An Executive’s View: Overview of major Machine Learning Algorithms

By this time I am assuming that you have a decent idea of what is Machine Learning and what are the broad types of its application and architecture.

If you want to brush up or you are a newbie to this arena, please feel free to have a look at my other article which introduces the subject.

Algorithms and sample business use cases

I’ve listed some of the most commonly used algorithms today — this list is not intended to be exhaustive. Additionally, a number of different models can often solve the same business problem. Conversely, the nature of an available data set often precludes using a model typically employed to solve a particular problem. For these reasons, the sample business use cases are meant only to be illustrative of the types of problems these models can solve.

Supervised Learning

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.

Use Cases 

• Understand product-sales drivers such as competition prices, distribution, advertisement, etc.

• Optimize price points and estimate product-price elasticities.

Logistic Regression

Extension of 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).

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. Helps in dimensionality reduction and identifying a better feature set.

Use Cases 

• Identify reasons for Client churn.

• Predict a sales lead’s likelihood of closing and reasons behind it.

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.

Use Cases 

• Provide a decisioning framework for hiring new employees.

• Understand product attributes that make a product most likely to be purchased.

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).

Use Cases 

• Analyse sentiment to assess product perception in the market.

• Create classifiers to filter spam mails.

Support Vector Machine

A technique that’s typically used for classification but can be transformed to perform regression. It draws an optimal division between classes (as wide as possible). It also can be quickly generalized to especially solve nonlinear problems.

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 advertisement banner.

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 classification.

Use Cases 

• Predict call volume in call centres for staffing decisions.

• Predict power usage in electrical-distribution grid.

AdaBoost

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.

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

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.

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 (math-software-based calculator units) make up three layers (an input layer, a hidden layer where calculations take place, and an output layer) that can be used to classify data or find the relationship between variables in regression problems.

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.

Unsupervised Learning

K-Means Clustering

Puts data into a number of groups (k) that each contain data with similar characteristics (as determined by the model, not in advance by humans).

Use Cases 

• Segment customers into groups by distinct characteristics (eg, age group) — for instance, to better assign marketing campaigns or prevent churn.

Gaussian Mixture Model

A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters).

Use Cases 

• Segment customers to better assign marketing campaigns using less-distinct customer characteristics (eg, product preferences).

• Segment employees based on likelihood of attrition.

Hierarchical Clustering

Splits or aggregates clusters along a hierarchical tree to form a classification system.

Use Cases 

• Cluster loyalty-card customers into progressively more micro-segmented groups.

• Inform product usage/development by grouping customers mentioning keywords in social-media data.

Apriori Algorithm

Apriori is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.

Use Cases 

• Identify frequent items that are bought together from a Walmart store.

• Find triggers for product takeu from customer’s life events.

Recommender Systems

Often uses cluster behavior prediction and apriori associations to identify the important data necessary for making a recommendation.

Use Cases 

• Recommend what movies consumers should view based on preferences of other customers with similar attributes.

• Recommend news articles a reader might want to read based on the article she or he is reading.

Conclusion

This article provided an introduction and overview of popular algorithms used to define and solve different problems using Machine Learning with their relevant business use cases.

These algorithms will be discussed in simplistically details in my further articles with delving a bit into their mathematical background as well as their implementations. Keep tuned for more!!

This article first appeared on my Medium blog. Please have a read of the original detailed article.