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This document discusses machine learning concepts including machine learning, artificial intelligence, supervised and unsupervised learning, and classification algorithms like logistic regression, K-nearest neighbors (KNN), decision trees, and random forests. It provides examples of how KNN and decision tree algorithms work, including an example of using KNN to classify an unknown record based on its distance to labeled records in a sample dataset.
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IEEE Presentation.pptx
- 1. Machine Learning Dr DebabrataSwain Assistant Professor (Senior Grade) CSE Department Pandit Deendayal Energy University Gandhinagar, Gujarat
- 2. Machine Learning • Machinelearning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
- 3. Traditional Computing vsMachine Learning
- 4. AI vs MLvs DL vs DS
- 5. Types of Learning •Supervised • Unsupervised
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- 10. Different Classification Algorithm •Logistic Regression • Knn Algorithm • Decision Tree • Random Forest
- 11. Logistic Regression • Itis a Machine Learning Algorithm used for Binary as well as Multiclass Classification.
- 17. K-NN Algorithm • Itis a classification algorithm. • For this algorithm we need to have some examples with correct groups called reference record. • For classifying an unknown point it finds the distance from all the points and looks for K- nearest points. • The class having majority is assigned to the unknown record.
- 18. Name Age GenderSport Ajay 32 M Football Mark 40 M Neither Sara 16 F Cricket Zaira 34 F Cricket Sachin 55 M Neither Rahul 40 M Cricket Pooja 20 F Neither Smith 15 M Cricket Laxmi 55 F Football Michael 15 M Football
- 19. • Before Trainingthe model we have to pre- process the data. • The gender column is carrying discrete data which is non-numeric. • We have to convert it to numeric categorical values Male=0, Female=1.
- 20. Name Age GenderSport Ajay 32 0 Football Mark 40 0 Neither Sara 16 1 Cricket Zaira 34 1 Cricket Sachin 55 0 Neither Rahul 40 0 Cricket Pooja 20 1 Neither Smith 15 0 Cricket Laxmi 55 1 Football Michael 15 0 Football
- 21. • Assume K=3(Number of Neighbours) • What will be the class of the test Record? Name- Anjelina Age- 5 Gender- Female (1) • To find the distance between the records distance formula is used.
- 22. • Distance betweenAjay and Anjelina= (5 − 32)2+(1 − 0)2 = 27.02
- 23. Name Age GenderSport Distance Ajay 32 0 Football 27.02 Mark 40 0 Neither 35.01 Sara 16 1 Cricket 11 Zaira 34 1 Cricket 9 Sachin 55 0 Neither 50.01 Rahul 40 0 Cricket 35.01 Pooja 20 1 Neither 15 Smith 15 0 Cricket 10 Laxmi 55 1 Football 50 Michael 15 0 Football 10.05
- 24. The most nearest3 records are- • Zaira (9)- Cricket • Smith (10)- Cricket • Michael (10.5)- Football Anjelina will have the class- Cricket (Majority)
- 25. Decision Tree • Whendataset is easily separable then we can use logistic regression to draw decision boundary for classification.
- 26. • But whenthe data points are not linearly separable or complex then we can not use logistic regression for classification.
- 28. • In suchkind of problems decision tree algorithm is used to draw different decision boundaries.
- 32. Random Forest • Itis a collection of different decision trees. • Each decision tree are trained using random batch of records out of the total records. • Now a number of classifiers we have. So when we want to find the class of an unknown record then all individual classifiers will give their decision and finally voting algorithm is used to select the class having majority votes.