Supervised Machine Learning.pptx
- 1. SUPERVISED MACHINE LEARNING Chandu Chandran MSc Computer Science Sree Narayana College, Cherthala
- 2. MACHINE LEARNING • Machine learning is a subset of AI, which enables the machine to automatically learn from data, improve performance from past experiences, and make predictions.
- 3. TYPES OF MACHINE LEARNING
- 4. SUPERVISED MACHINE LEARNING • In supervised learning applies the same concept as a student learns in the supervision of the teacher. • Supervised learning is the types of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. • The labelled data means some input data is already tagged with the correct output. • The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y).
- 5. HOW SUPERVISED LEARNING WORKS? • In supervised learning, models are trained using labelled dataset, where the model learns about each type of data. Once the training process is completed, the model is tested on the basis of test data (a subset of the training set), and then it predicts the output. • Suppose we have an input dataset of cats and dog images. So, first, we will provide the training to the machine to understand the images, such as the shape & size of the tail of cat and dog, Shape of eyes, color, height (dogs are taller, cats are smaller), etc. • After completion of training, we input the picture of a cat and ask the machine to identify the object and predict the output. Then machine find that it's a cat. So, it will put it in the Cat category.
- 6. WORKING DIAGRAM OF SUPERVISED LEARNING
- 7. STEPS INVOLVED IN SUPERVISED LEARNING: • First Determine the type of training dataset • Gather the labelled training data. • Split the training dataset into training dataset, test dataset, and validation dataset. • Determine the input features of the training dataset. • Determine the suitable algorithm for the model. • Execute the algorithm on the training dataset. • Evaluate the accuracy of the model by providing the test set. • If the model predicts the correct output, which means our model is accurate.
- 8. TYPES OF SUPERVISED MACHINE LEARNING ALGORITHMS: • Supervised learning can be further divided into two types of problems:
- 9. Types of Regression • A regression is a statistical technique that relates a dependent variable to one or more independent (explanatory) variables. • It is used for the prediction of continuous variables Eg:- Weather forecasting, Market Trends, etc. REGRESSION • Linear Regression • Regression Trees • Non-Linear Regression • Polynomial Regression
- 10. • Linear Regression: linear correlation of the explanatory variables and dependent variable. Eg:- Age and height can be described using a linear regression model. • Regression Trees: A regression tree refers to an algorithm where the target variable is and the algorithm is used to predict its value. Eg:- Predict the selling prices of a residential house • Non-Linear Regression: Non-Linear Regression is not a straight-line or direct relationship between an independent variable and a dependent variable. Eg:- Predict population growth over time. • Polynomial Regression: Multiple linear regression relationship is called polynomial regression. Eg:- Widely applied to predict the spread rate of COVID-19 and other infectious diseases.
- 11. CLASSIFICATION • Classification algorithms are used when the output variable is categorical, which means there are two classes such as Yes-No, Male-Female, True-false, etc. Types of Classification • Decision Trees • Random Forest • Logistic Regression • Support vector Machines
- 12. • Decision Trees: A decision tree is a type of supervised machine learning used to categorize or make predictions based on how a previous set of questions were answered. Eg:- online shopping portal where we get several recommendations based on what we are buying.
- 13. • Random Forest: A random forest is a collection of decision trees. whose results are aggregated into one final result. Eg:- classifying whether an email is “spam” or “not spam”
- 14. • Logistic Regression: Logistic regression estimates the probability of an event occurring, such as voted or didn't vote, based on a given dataset of independent variables.. Eg:- Political candidate will win or lose an election.
- 15. • Support Vector Machines: The data points that are the closest to the hyperplane and which affect the position of the hyperplane are termed as Support Vector. Eg:- handwriting recognition, face detection, email classification,
- 16. ADVANTAGES OF SUPERVISED LEARNING: • Since supervised learning work with the labelled dataset so we can have an exact idea about the classes of objects. • These algorithms are helpful in predicting the output on the basis of prior experience. DISADVANTAGES OF SUPERVISED LEARNING: • These algorithms are not able to solve complex tasks. • It may predict the wrong output if the test data is different from the training data. • It requires lots of computational time to train the algorithm.
- 17. APPLICATIONS OF SUPERVISED LEARNING • Image Segmentation • Medical Diagnosis • Fraud Detection • Spam detection • Speech Recognition • Predicting repair of equipment • Sales Forecasting •
- 18. THANK YOU!