Random Forest Algorithm: A Machine Learning ALgorithm.pdf

A ppt about Random Forest Algorithm

1. RANDOM
FOREST
ALGORITHM
-SHALINI, REEENA &
SUHAANA

2. CONTENTS
01
Machine Learning
and its Types
04
Decision Tree 02
Random Forest
Algorithm
03
05
Why Random
Forest?
Applications of
Random Forest

3. Machine Learning
Machine Learning
●Machinelearningisasubsetofartificialintelligence(AI)thatfocusesonthe
developmentofalgorithmsandstatisticalmodelsthatenablecomputersto
improvetheirperformanceonaspecifictaskthroughexperience.
●Insteadofbeingexplicitlyprogrammedforatask,machineslearnfromdataand
makepredictionsordecisionsbasedonpatternsandrelationshipsdiscoveredin
thatdata.

4. Types of machine
learning
Types of Machine Learning
Mobile banking
Unsupervised Learning
Reinforcement Learning
Supervised Learning

5. Decision Tree
Decision Tree
●Adecisiontreeisatypeofsupervisedmachinelearningusedtocategorizeor
makepredictionsbasedonhowaprevioussetofquestionswereanswered.
●Themodelisaformofsupervisedlearning,meaningthatthemodelistrainedand
testedonasetofdatathatcontainsthedesiredcategorization.
●Decisiontreesimitatehumanthinking,soit’sgenerallyeasyfordatascientiststo
understandandinterprettheresults.

6. Decision Tree Terminologies

7. Decision Tree Example

8. Decision Tree Example

9. Random Forest
●ARandomForestAlgorithmisasupervisedmachinelearningalgorithmthatis
extremelypopularandisusedforClassificationandRegressionproblemsin
MachineLearning.
●The“forest”itbuildsisanensembleofdecisiontrees,usuallytrainedwiththe
baggingmethod.Thegeneralideaofthebaggingmethodisthatacombinationof
learningmodelsincreasestheoverallresult.
●RandomForestbuildsmultipledecisiontreesandmergesthemtogethertogeta
moreaccurateandstableprediction.

10. Working of Random Forest Algorithm
01Select random samples from a given data or training set.
02
This algorithm will construct a decision tree for every
training data.
03Voting will take place by averaging the decision tree.
04
Finally, select the most voted prediction result as the
final prediction result.

11. Working of Random Forest Algorithm

12. Random Forest Example
CONDITION
Colour== Red ?
Diameter == 3
Colour== Orange?
Diameter == 1
TRAINING DATASET
COLOR DIAMETER LABEL
Red 3 Apple
Red 1 Cherry
Red 3 Apple
Orange 3 Orange
Red 1 Cherry

13. Random Forest Example
Determine the name of the fruit ?

14. Random Forest Example

15. Random Forest Example

16. Why Random Forest ?

17. Applications of Random Forest
04Healthcare
03
Customer Churn
Prediction
02
Image and Speech
Recognition
01Anomaly Detection
Applications of
Random Forest

18. Thank You