The 8 Different AI Models You Need to Understand Today

Artificial intelligence (AI) is transforming industries across the board. Companies that fail to leverage AI risk falling behind the competition and missing key opportunities to improve efficiency, attract customers, and increase profits. With dozens of AI techniques available, the key is understanding which to apply for your unique needs.

In this comprehensive guide, we cover the 8 most important AI models to know in today’s data-driven world. For each model we’ll unpack how it works, key benefits, limitations, and ideal use cases across industries like finance, marketing, healthcare, and more. Let’s dive in!

What Are AI Models and Why Do They Matter?

AI models are mathematical frameworks powered by algorithms that allow machines to learn from data in order to make predictions or decisions without explicit programming.

In essence, AI models aim to simulate elements of human learning and intelligence. They ingest large volumes of data, identify patterns and relationships within it, and use insights derived to accomplish specified tasks – from powering search algorithms to approving loans.

The value of AI models stems from their ability to quickly process data and automate complex analytical tasks that would be impossible for humans to handle manually. As AI expert and professor Andrew Ng explains:

“AI is the new electricity. Just as electricity transformed industries 100 years ago, AI will now do the same.”

Familiarity with leading AI techniques is becoming mandatory knowledge for well-rounded data scientists and tech professionals. Understanding model capabilities and limitations allows for more strategic application to unlock value.

Below we cover 8 essential models spanning predictive analytics, deep learning neural networks, and beyond.

1. Linear Regression

Linear regression is likely the most popular and widely-used AI model today. It shines when estimating numerical outcomes like sales, demand forecasting, or predicting house prices.

Here’s a simple example…

Let’s say a real estate investor wants to create an AI price prediction model for houses in Seattle. She compiles a dataset of recently sold homes that includes features like square footage, number of bedrooms, location, etc. as well as the eventual sales price.

This becomes the training data for a linear regression model. It will learn the correlation between input home features and the target variable – sales price. Once trained, the model can take characteristics of any new house on the market and predict what the sale price will be.

Linear regression models excel at uncovering relationships between numerical variables. Industry use cases span:

  • Financial services – credit & operational risk modeling, algorithmic trading
  • Energy – predicting electricity consumption patterns
  • Insurance – risk assessment for pricing and underwriting
  • Supply chain – demand forecasting models

The main limitation is assuming a linear relationship between input and target variables, which doesn’t always reflect real-world complexity. Performance also tends to lag with small datasets.

2. Logistic Regression

While their names are similar, linear and logistic regression models have distinct differences.

Logistic regression is ideal for predicting binary outcomes like pass/fail, true/false, alive/dead, or loan default predictions. Outputs reflect probabilities – for instance a customer has an 85% chance of loan default based on credit history.

Here’s an example…

An insurance firm develops automated underwriting models to determine policies for new customers. Historical customer data with various risk attributes (age, location, income, etc.) is fed to a logistic regression model, along with the binary target variable – whether the customer went on to file a claim.

Once trained, the logistic regression model can ingest a new potential customer’s information and output the probability they will file an insurance claim. This drives automated underwriting decisions.

Key logistic regression benefits include:

  • Probability-based outputs reflect real-world uncertainty
  • Handles nonlinear complex feature relationships
  • Computationally lightweight and fast to execute

The downside is that performance suffers with very high-dimensional data. Logistic models also tend to be less accurate than alternatives like random forests or neural networks.

3. Deep Neural Networks

Neural networks are AI models composed of interconnected nodes mimicking neurons in the human brain. Information flows through the network layers transforming input data into predictions on the other end.

Deep neural networks contain many hidden layers between the input and output allowing for highly sophisticated data processing not possible with earlier single layer networks.

Diagram showing structure of a deep neural network with input, multiple hidden layers, and output

A deep neural network with multiple hidden layers (Nicholas Samuel)

Here are some defining traits of deep neural networks:

  • Each node assigns “weights” to inputs based on predictive power – this determines how data flows through the model
  • The network continually adjusts weights through backpropagation to improve accuracy
  • Capable of handling unstructured data like images, video, audio and text
  • Requires massive training data and compute power

Thanks to recent leaps in data accumulation and computing, deep neural networks now power many familiar AI applications:

  • Image recognition – identifying objects and faces in digital photos
  • Language translation – Google Translate processes billions of sentences
  • Autonomous driving systems – sensors feed real-time image, lidar and radar data to steering and brake controllers

Challenges include interpretability (difficult for humans to understand internal workings) and tendency to overfit training data. Deep network performance is also highly dependent on quality data at scale.

4. Decision Trees

Imagine having to make a complex decision but wishing you could clearly lay out all possible outcomes step-by-step beforehand.

That’s essentially what decision trees allow AI models to do. These trees contain layers of branching conditional logic that account for every potential scenario based on the input data.

decision tree model showing branching logic

A sample decision tree (Nicholas Samuel)

Here’s an example…

An e-commerce company needs a model to determine what discount amount if any to offer website visitors in real-time. Data inputs might include user demographics, browsing history, device type, etc.

A decision tree model would be trained on historical checkout data paired with whether discounts were eventually applied. Complex branching logic accounts for all potential customer attribute combinations.

When a new visitor loads the website, the decision tree rapidly evaluates their unique characteristics to determine the optimal discount using previously learned patterns.

Benefits of decision tree models include:

  • Human readable logic – easily explained
  • Captures nonlinear complex relationships
  • Computationally lightweight and fast
  • No data normalization required
  • Handles numerical and categorical data types

Downsides center around depth and purity. Performance declines as trees become excessively complex. Pruning strategies must simplify logic while retaining accuracy. Decision trees also tend to overfit training data more than ensemble methods.

5. Random Forests

What if one decision tree isn’t enough? Random forests take things up a notch by combining numerous decision trees to boost predictive accuracy.

They work by training a collection of decorrelated decision trees on separate data samples and then averaging their results. Each constituent tree identifies different patterns from the data slices. Aggregating multiple perspectives minimizes bias and overfitting effects often seen in single estimator models.

Let’s walk through an example…

A healthcare organization needs to predict which incoming patients are most at risk for hospital readmission. Hundreds of decision trees are trained on randomized patient health record samples with readmission flags.

A new patient’s information flows down each unique decision tree logic structure generating a readmission probability. The forest combines probabilities to produce a final output reflecting insights from all its trees.

Key random forest benefits:

  • Virtually eliminates overfitting issues
  • Captures complex nonlinear relationships
  • Computes predictions quickly in parallel
  • Works for both classification and regression tasks

Performance gains do exhibit diminishing returns though. After a point, more trees cease to boost accuracy while dragging down efficiency. Random forests also remain somewhat opaque given so many trees.

Overall, combining multiple decision trees addresses flaws inherent in single models – making random forests a versatile, accurate and scalable AI technique.

6. Naive Bayes Classifiers

Don’t let the name fool you – Naive Bayes classifiers remain an extremely effective approach for modeling probability based predictions.

These models apply Bayes‘ probability theorem using strong independence assumptions between input variables. In other words, inputs are assumed to contribute to the outcome prediction independently of each other.

Here’s a Naive Bayes example:

A manufacturing firm needs help triaging equipment failure calls to optimize repair dispatching. 100 work orders with clear symptom patterns and machine types are fed to a Naive Bayes model for initial training.

The model computes failure probability outputs for combinations of newly reported failure symptoms and unit types based on their frequencies within the training data. Highest probability calls get routed to repair crews first.

Despite its simplicity, the naive Bayes model performs surprisingly well across many real-world use cases:

  • Email spam detection
  • Document categorization
  • Diagnostic systems – combine observed symptoms to identify likely illness
  • Product recommendation engines – based on purchase history

The naive assumption of predictor independence is key to model interpretability and computational efficiency. But it can hurt accuracy if interaction effects strongly influence the target variable.

Naive Bayes works best when:

  • Training data features have low correlation
  • Dataset is very large relative to number of features
  • Outputs reflect probability versus deterministic predictions

7. K-Nearest Neighbors

Ever used a navigation app that compared arrival times for route options? The one promising earliest arrival represents the “nearest neighbor”.

K-nearest neighbors (KNN) is an AI model that classifies data points based on proximity to labeled examples from the training dataset.

To make a prediction on a new data point:

  1. Calculate distances to all points in the training dataset
  2. Select the “K” closest labeled points as a reference peer group
  3. Assign output value (classification) based on majority of the K nearest neighbor outputs

K-nearest neighbors diagram showing new data point classification based on proximity to groups of training examples

New data points are classified based on nearest training examples (Nicholas Samuel)

Here’s an example…

An insurance firm develops automated customer segmentation models using past demographic, behavioral and claims data. Once the KNN model is trained, new customers can be instantly grouped upon signup to support targeted messaging and tiered policies.

KNN shines when:

  • Training data is abundant
  • Additional data becomes available over time
  • Data points concentrate together in groupings
  • Speed is important

Drawbacks center around algorithmic complexity and memory constraints for massive databases. Finding nearest neighbors grows exponentially more difficult as datasets scale to billions of points.

Still – when applied to suitable use cases – KNN delivers fast, adaptable, and intuitive classification power.

8. Linear Discriminant Analysis

Linear discriminant analysis (LDA) brings the versatility of linear regression models to classification tasks.

The approach searches training data to identify linear combinations of input variables that best separate output classes. LDA computes one or more optimal dividing lines called discriminants.

Here’s an example:

Linear discriminant analysis example diagram showing classifier line dividing data points into groups

LDA placing a linear separator between data classes (Nicholas Samuel)

A bank uses customer financial transactions to predict credit card fraud. LDA computes the discriminator that best divides legitimate and fraudulent groups within the training dataset. Going forward, this discriminant function flags any new outlier transactions on one side of the line.

LDA’s versatility underpins uses cases across:

  • Image recognition
  • Psychometrics – design questionnaires to reveal groupings
  • Document classification
  • Manufacturing – assign new products to predefined quality grade brackets

Its simplicity can also be a curse. LDA forced to fit linear boundaries will underperform for more complex nonlinear class separators. Think round/square peg scenarios.

Still – ease of interpretation and training makes LDA a widely used introductory approach for discrimination tasks.

Key Takeaways: Choosing AI Models for Business Success

Every AI model has pros and cons. There is no universally superior methodology – the best approach depends entirely on your specific prediction problem and data realities.

With so many models now mainstream, here are 5 closing recommendations when evaluating options:

1. Clearly define the prediction task – Will the model output numerical estimates or classifications? Are outputs definitive or probability-based? Get very clear on objectives.

2. Assess input data compatibilities – Data rarely comes pristine and model-ready. Check for gaps, extreme outliers, mixed formats, and small sample sizes that could impact solution viability.

3. Play to model strengths while mitigating limitations – The overview of models above highlights key factors on both fronts to guide your positioning.

4. Validate with test data – Does it match reality? No matter how promising model outputs appear, run experiments with new test data separate from initial training sets to confirm real-world performance.

5. Iteratively improve – Even validated models degrade over time as relationships and patterns change. Continually retrain with new data to keep accuracy sharp.

While hands-on experimentation ekes out the very best approach – I hope this guide provides a head start grasping available options. Well implemented models will transform capabilities and unlock immense value – so put that new knowledge to work!

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