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Types of Machine Learning

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Saved by /pd
on February 14, 2020 at 9:50:55 pm
 
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 

 

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 classifation 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   
Naive Bayes   
Support vector machine   
Random forest   
AdaBoost   
Gradient-boosting trees   
10  Simple neural network   
     
     
     
     
     

 

 

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