Supervised learning and unsupervised learning
- 1. MACHINE LEARNING A ARUNA KUMARI CSE Department Assistant professor VNRVJIET
- 2. Supervised learning: Supervised learning is the learning of the model where with input variable ( say, x) and an output variable (say, Y) and an algorithm to map the input to the output. That is, Y = f(X) Why supervised learning? The basic aim is to approximate the mapping function (mentioned above) so well that when there is a new input data (x) then the corresponding output variable can be predicted. It is called supervised learning because the process of an learning (from the training dataset) can be thought of as a teacher who is supervising the entire learning process. Thus, the “learning algorithm” iteratively makes predictions on the training data and is corrected by the “teacher”, and the learning stops when the algorithm achieves an acceptable level of performance (or the desired accuracy). Example of Supervised Learning Suppose there is a basket which is filled with some fresh fruits, the task is to arrange the same type of fruits at one place. Also, suppose that the fruits are apple, banana, cherry, grape. Suppose one already knows from their previous work (or experience) that, the shape of each and every fruit present in the basket so, it is easy for them to arrange the same type of fruits in one place. Here, the previous work is called as training data in Data Mining terminology. So, it learns the things from the training data. This is because it has a response variable which says y that if some fruit has so and so features then it is grape, and similarly for each and every fruit. This type of information is deciphered from the data that is used to train the model.This type of learning is called Supervised Learning. Such problems are listed under classical Classification Tasks. Unsupervised Learning: Unsupervised learning is where only the input data (say, X) is present and no corresponding output variable is there. Why Unsupervised Learning? The main aim of Un supervised learning is to model the distribution in the data in order to learn more about the data. It is called so, because there is no correct answer and there is no such teacher (unlike supervised learning). Algorithms are left to their own devises to discover and present the interesting structure in the data.
- 3. Example of Unsupervised Learning Again, Suppose there is a basket and it is filled with some fresh fruits. The task is to arrange the same type of fruits at one place. This time there is no information about those fruits beforehand, its the first time that the fruits are being seen or discovered So how to group similar fruits without any prior knowledge about those. First, any physical characteristic of a particular fruit is selected. Suppose color. Then the fruits are arranged on the basis of the color. The groups will be something as shown below: RED COLOR GROUP: apples & cherry fruits. GREEN COLOR GROUP: bananas & grapes. So now, take another physical character say, size, so now the groups will be something like this. RED COLOR AND BIG SIZE: apple. RED COLOR AND SMALL SIZE: cherry fruits. GREEN COLOR AND BIG SIZE: bananas. GREEN COLOR AND SMALL SIZE: grapes. The job is done! Here, there is no need to know or learn anything beforehand. That means, no train data and no response variable. This type of learning is known as Unsupervised Learning. Difference b/w Supervised and Unsupervised Learning: Supervised Learning Unsupervised Learning Input Data Uses Known and Labeled Data as input Uses Unknown Data as input Computational Complexity Very Complex Less Computational Complexity Real Time Uses off-line analysis Uses Real Time Analysis of Data Number of Classes Number of Classes are known Number of Classes are not known Accuracy of Results Accurate and Reliable Results Moderate Accurate and Reliable Results Supervised and Unsupervised learning Supervised learning
- 4. Supervised learning as the name indicates the presence of a supervisor as a teacher. Basically supervised learning is a learning in which we teach or train the machine using data which is well labeled that means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data. For instance, suppose you are given a basket filled with different kinds of fruits. Now the first step is to train the machine with all different fruits one by one like this: If shape of object is rounded and depression at top having color Red then it will be labeled as –Apple. If shape of object is long curving cylinder having color Green-Yellow then it will be labeled as –Banana. Now suppose after training the data, you have given a new separate fruit say Banana from basket and asked to identify it.
- 5. Since the machine has already learned the things from previous data and this time have to use it wisely. It will first classify the fruit with its shape and color and would confirm the fruit name as BANANA and put it in Banana category. Thus the machine learns the things from training data(basket containing fruits) and then apply the knowledge to test data(new fruit). Supervised learning classified into two categories of algorithms: Classification: A classification problem is when the output variable is a category, such as “Red” or “blue” or “disease” and “no disease”. Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”. Supervised learning deals with or learns with “labeled” data. Which implies that some data is already tagged with the correct answer. Types:- Regression Logistic Regression Classification Naïve Bayes Classifiers Decision Trees Support Vector Machine Advantages Supervised learning allows collecting data and produce data output from the 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.
- 6. Unsupervised learning Unsupervised learning is the training of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of machine is to group unsorted information according to similarities, patterns and differences without any prior training of data. Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore machine is restricted to find the hidden structure in unlabeled data by our-self. For instance, suppose it is given an image having both dogs and cats which have not seen ever. Thus the machine has no idea about the features of dogs and cat so we can’t categorize it in dogs and cats. But it can categorize them according to their similarities, patterns, and differences i.e., we can easily categorize the above picture into two parts. First first may contain all pics having dogs in it and second part may contain all pics having cats in it. Here you didn’t learn anything before, means no training data or examples. It allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with unlabelled data. Unsupervised learning classified into two categories of algorithms: Training Data Features Vector Algorithm Model
- 7. Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y. Types of Unsupervised Learning:- Clustering 1. Exclusive (partitioning) 2. Agglomerative 3. Overlapping 4. Probabilistic Clustering Types:- 1. Hierarchical clustering 2. K-means clustering 3. K-NN (k nearest neighbors) 4. Principal ComponentAnalysis 5. Singular Value Decomposition 6. Independent Component Analysis Supervised vs. Unsupervised Machine Learning Parameters Supervised machine learning Unsupervised machine learning Input Data Algorithms are trained using labeled data. Algorithms are used against data which is not labelled Computational Complexity Simpler method computationally complex Accuracy Highly accurate Less accurate