Types of Machine Learning- Tanvir Siddike Moin
- 1. Types of Machine Learning Tanvir Siddike Moin 1
- 2. Machine Learning A machine is said to be learning from past Experiences(data feed in) with respect to some class of tasks if its Performance in a given Task improves with the Experience. For example, A machine has to predict whether a customer will buy a specific year or not. The machine will do it by looking at the previous knowledge/past experiences i.e the data of products that the customer had bought every year and if he buys Antivirus every year, then there is a high probability that the customer is going to buy an antivirus this year as well. 2
- 3. Types of ML 3 Task Driven Data Driven Learn from Mistakes Machine Learning systems can be classified according to the amount and type of supervision they get during training.
- 4. Supervise d Learning 4 In supervised learning, the training data you feed to the algorithm includes the desired solutions, called labels. And Supervised learning model is getting trained on a labelled dataset. A labelled dataset is one that has both input and output parameters. In this type of learning both training and validation, datasets are labelled.
- 5. 5 Both the above figures have labelled data set – Figure A: It is a dataset of a shopping store that is useful in predicting whether a customer will purchase a particular product under consideration or not based on his/ her gender, age, and salary. Input: Gender, Age, Salary Output: Purchased i.e. 0 or 1; 1 means yes the customer Figure B: It is a Meteorological dataset that serves the purpose of predicting wind speed based on different parameters. Input: Dew Point, Temperature, Pressure, Relative Humidity, Wind Direction Output: Wind Speed
- 6. Training the system: • While training the model, data is usually split in the ratio of 80:20 i.e. 80% as training data and rest as testing data. • In training data, we feed input as well as output for 80% of data. The model learns from training data only. • Different machine learning algorithms are used to build the ML model. • By learning, it means that the model will build some logic of its own. • Once the model is ready then it is good to be tested. • At the time of testing, the input is fed from the remaining 20% data which the model has never seen before, the model will predict some value and at last, will compare it with actual output and calculate the accuracy. 6
- 7. Types of Supervised Learning Classification It is a Supervised Learning task where output is having defined labels(discrete value). For example in above Figure A, Output – Purchased has defined labels i.e. 0 or 1; 1 means the customer will purchase and 0 means that customer won’t purchase. The goal here is to predict discrete values belonging to a particular class and evaluate them on the basis of accuracy. • It can be either binary or multi-class classification. In binary classification, the model predicts either 0 or 1; yes or no but in the case of multi-class classification, the model predicts more than one class. • Example: Gmail classifies mails in more than one class like social, promotions, updates, forums. 7
- 8. Regressio n • Another typical task is to predict a target numeric value, such as the price of a car, given a set of features (mileage, age, brand, etc.) called predictors. • It is a Supervised Learning task where output is having continuous value. • Example in above Figure B, Output – Wind Speed is not having any discrete value but is continuous in the particular range. • The goal here is to predict a value as much closer to the actual output value as our model can and then evaluation is done by calculating the error value. The smaller the error the greater the accuracy of our regression model. 8
- 9. 9 Example of Supervised Learning Algorithms: Clustering k-Nearest Neighbors Linear Regression Logistic Regression Decision Trees and Random Forests Gaussian Naive Bayes Support Vector Machines (SVMs)
- 10. Advantages: • Supervised learning allows collecting data and produces data output from previous experiences. • Helps to optimize performance criteria with the help of experience. • Supervised machine learning helps to solve various types of real-world computation problems. Disadvantages:- • Classifying big data can be challenging. • Training for supervised learning needs a lot of computation time. So, it requires a lot of time. 10
- 11. Unsupervised Learning • It’s a type of learning where we don’t give a target to our model while training i.e. training model has only input parameter values. The model by itself has to find which way it can learn. • The training data is unlabeled 11
- 12. • Data-set in Figure A is mall data that contains information of its clients that subscribe to them. Once subscribed they are provided a membership card and so the mall has complete information about the customer and his/her every purchase. Now using this data and unsupervised learning techniques, the mall can easily group clients based on the parameters we are feeding in. 12
- 13. • Training data we are feeding is – • Unstructured data: May contain noisy(meaningless) data, missing values, or unknown data • Unlabeled data: Data only contains a value for input parameters, there is no targeted value(output). It is easy to collect as compared to labeled one in the Supervised approach. 13
- 14. Types of Unsupervised Learning 14 Clustering: Broadly this technique is applied to group data based on different patterns, our machine model finds. Algorithm to try to detect groups of similar visitors. At no point do you tell the algorithm which group a visitor belongs to: it finds those connections without your help. For example, it might notice that 40% of your visitors are males who love comic books and
- 15. Associati on This technique is a rule-based ML technique that finds out some very useful relations between parameters of a large data set. For e.g. shopping stores use algorithms based on this technique to find out the relationship between the sale of one product w.r.t to others sales based on customer behavior. Once trained well, such models can be used to increase their sales by planning different offers. • Some algorithms: Clustering: • K-Means Clustering • Hierarchical Cluster Analysis (HCA) • Expectation Maximization Visualization and dimensionality reduction • Principal Component Analysis (PCA) • Kernel PCA • Association rule learning • Apriori • Eclat 15
- 16. • Clustering: algorithm to try to detect groups of similar visitors. • Visualization algorithms are also good examples of unsupervised learning algorithms • You feed them a lot of complex and unlabeled data, and they data, and they output a 2D or 3D representation of your data of your data that can easily be plotted. • These algorithms try to preserve as much structure as they structure as they can (e.g., trying to keep separate clusters in separate clusters in the input space from overlapping in the overlapping in the visualization), so you can understand how the understand how the data is organized and perhaps identify perhaps identify unsuspected patterns. • Dimensionality reduction, in which the goal is to simplify the data without losing too much information. One way to do this is way to do this is to merge several correlated features into one. features into one. For example, a car’s mileage may be very may be very correlated with its age, so the dimensionality dimensionality reduction algorithm will merge them into one them into one feature that represents the car’s wear and tear. wear and tear. This is called feature extraction. extraction. 16
- 17. 17 Anomaly detection — for example, detecting unusual credit card transactions to prevent fraud, catching manufacturing defects, or automatically removing outliers from a dataset before feeding it to another learning algorithm. The system is trained with normal instances, and when it sees a new instance it can tell whether it looks like a normal one or whether it is likely an anomaly
- 18. • Association Rule Learning, in which the goal is to dig into large amounts of data and discover interesting discover interesting relations between attributes. For attributes. For example, suppose you own a a supermarket. Running an association rule on your rule on your sales logs may reveal that people who people who purchase barbecue sauce and potato chips potato chips also tend to buy steak. Thus, you may want Thus, you may want to place these items close to each close to each other. 18
- 20. Semi- supervised Learning: • Some algorithms can deal with partially labeled training data, usually a lot of unlabeled data and a little bit of labeled data. This is called semisupervised learning • We can use the unsupervised techniques to predict labels and then feed these labels to supervised techniques. This technique is mostly applicable in the case of image data sets where usually all images are not labeled. 20
- 21. Reinforcemen t Learning: • The learning system, called an agent in this context, can observe the environment, select and perform actions, and get rewards in return (or penalties in the form of negative rewards. • It must then learn by itself what is the best strategy, called a policy, to get the most reward over time. A policy defines what action the agent should choose when it is in a given situation. 21
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- 23. Batch and Online Learning Batch learning • In batch learning, the system is incapable of learning incrementally: it must be trained using all the available data. This will generally take a lot of time and computing resources, so it is typically done offline. First the system is trained, and then it is launched into production and runs without learning anymore; it just applies what it has learned. This is called offline learning. • Online learning • In online learning, you train the system incrementally by feeding it data instances sequentially, either individually or by small groups called mini-batches 23
- 24. Instance-Based Versus Model-Based Learning Instance-based learning • If you were to create a spam filter this way, it would just flag all emails that are identical to emails that have already been flagged by users— not the worst solution, but certainly not the best. • Instead of just flagging emails that are identical to known spam emails, your spam filter could be programmed to also flag emails that are very similar to known spam emails. This requires a measure of similarity between two emails. A (very basic) similarity measure between two emails could be to count the number of words they have in common. The system would flag an email as spam if it has many words in common with a known spam email. • This is called instance-based learning: the system learns the examples by heart, then generalizes to new cases using a similarity measure 24
- 25. Model- based learnin g 25 • Another way to generalize from a set of examples is to build a model of these examples, then use that model to make predictions. This is called model-based learning