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Supervised Machine Learning Techniques
- 1. SUPERVISED MACHINE LEARNING TECHNIQUES TARARAM MCA 6th SEM
- 2. 1. Introduction Terminology, type ofsupervised learning, Common Algorithms & it’s applications 2
- 3. Instructions 3 ▸ Supervised machinelearning is subfields machine learning. ▸ Supervised learning uses patterns to predict label values on additional unlabeled data. ▸ Supervised learning is to use historical data to predict statistically likely future events.
- 4. Cont.. 4 ▸ Supervised learning,in the context of artificial intelligence (AI) and machine learning, is a type of system in which both input and desired output data are provided. ▸ In supervised learning, the system tries to learn from the previous examples and labeled data that are given.
- 5. Learning Process 5 Training Data Machinelearning Algorithm ClassifierNew Data Prediction
- 6. Supervised Vs UnsupervisedMachine Learning Technique Based On SML UML Input Data Trained using labelled data Trained using unlabelled data Accuracy of the Result More accurate and reliable Less acurate and reliable Number of classes Known Unknown Real Time Learning Learning tasks place off-line Learning takes place in real time. 6
- 7. Terminology 7 ▸ Data ▸ Problemsolving tools ▸ Combinations of computer science and engineering and statistics ▸ Optimize performance criteria using past experience
- 8. Types of SupervisedMachine 8 Supervised Machine Learning Algorithms Classification Supervised Machine Learning Algorithms Regression Supervised Mahine Learning Algorithms
- 9. Classification Supervised MachineLearning Algorithms ▸ Classification algorithms are used to classify a records. ▸ A classification problem is when the output variable is a category or a group. 9
- 10. Regression Supervised MachineLearning Technique ▸ Regression Algorithms are used to calculate numeric values. ▸ A regression problem is when the output variable is a real value, such as “Rupees” or “height.” 10
- 11. Comman Algorithms 11 ▸ LinearRegression ▸ Decision Trees ▸ Logistic Regression ▸ Naïve Bayes classifier
- 12. Linear Regression 12 ▸ Itis used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). ▸ Establish relationship between independent and dependent variables by fitting a best line. ▸ This best fit line is known as regression line.
- 13. Cont.. 13 ▸ A Standardand simple mathematical techniques for predicting numeric outcomes. ▸ Oldest and most widely predictive model
- 14. Decision Trees ▸ Itis mostly used for classification problems. ▸ Decision trees classify instances or examples by starting at the root of the tree and moving through it until a leaf node ▸ Decision tree is a classifier in the form of a tree structure. 14
- 15. Decision Tree 15 Age Young MiddleOld Has_Job Own_House Credit_Rating True false True False Fair Good Yes No Yes No No Yes Excellence Yes
- 16. Logistic Regression ▸ Itis used to estimate discrete values (Binary values like 0/1, yes/no, true/false) based on given set of independent variable(s). ▸ It predicts the probability of occurrence of an event by fitting data to a logit function. ▸ Its output values lies between 0 and 1 (as expected). 16
- 17. Naïve Bayes classifier ▸Naïve Bayes Classifier technique based on Bayes theorem. ▸ Naïve Bayesian model is easy to build. ▸ Naïve Bayes Classifier is used in large data set. 17
- 18. Applications ▸ Speech Recognition ▸Effective Web Search ▸ Machine Translation(Languages translation) ▸ Email Spam Filtering ▸ Fraud Detection ▸ Medical Diagnosis 18
- 19. Cont.. ▸ Stock MarketAnalysis ▸ Structural Health Monitoring ▸ Image Search (Similarity) ▸ Recommendation System 19
- 20.