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Types of Machine Learning

Page history last edited by /pd 1 week, 5 days ago
Vector  Structured Learning  Unstructured Learning  Reinforced Learning 
What

Needs Humans 

Learn relationships

Needs explicit output 

explore data

no explicit output 

perform a task

get rewards 

When to use 

Classify data - humans

define type of behaviour

algorithms form new data sets  

No data classification 

Finds patterns with data set 

reclassification data to  form new data

No Training data

NO end state

Need to interact with model to derive output 

How to use 

Label all input data

define output

find connections between data sets

apply algos to form new data sets 

Unlabeled data

infers structures from data

ID's groups of data with same behaviour

Takes an action/task

gets a reward optimzation based on best series of actions 

 

Various Algo's and business case 

 

Supervised learning: Algorithms and sample business use cases

 

Liner regression 

Highly interuptable . need to have past relationship identified 

for the output variable

helps predicate future output variables

understand sales drivers such as price,  ad's or distribution

optimzation of price points 

Logistic regression 

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)

Predict if a skin lesion is benign or malignant based on its characteristics (size, shape, color, etc) 

Linear/quadratic discriminant analysis 

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

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

a decision framework for hiring new employees 

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

Naive Bayes 

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)

Analyze sentiment to assess product perception in the market

Create classifiers to filter spam emails

 

Support vector machine 

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 solve nonlinear problems

 

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

 

Random forest 

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 (

Predict call volume in call centers for staffing decisions

Predict power usage in an 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

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

Forecast product demand and inventory levels

Predict the price of cars based on their characteristics (eg, age and mileage)

 
10 

Simple neural network

 Model in which artificial neurons (software-based calculators) 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

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

 

 

 

 

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