Mis End Term Exam Theory Concepts
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The document provides an overview of concepts and topics to be covered in the MIS End Term Exam for AI and A2 on February 6th 2020, including: decision trees, classifier algorithms like ID3, CART and Naive Bayes; supervised and unsupervised learning; clustering using K-means; bias and variance; overfitting and underfitting; ensemble learning techniques like bagging and random forests; and the use of test and train data.
Mis End Term Exam Theory Concepts
- 1. MIS End Term Exam for AI and A2 on Thursday ,6th February 2020 Concepts Please read the handwritten scanned notes also
- 2. End Term Syllabus • Decision Trees – Graphical Representation – Root Node , Internal Nodes , Leaf • Level ,Height and Depth of Node • Decision Tree – As Classifier • Classifier Case Study /Numerical using 3 Classifier Algorithms - ID3 - CART -Naïve Bayes
- 3. End Term Syllabus Cont • Supervised , Unsupervised and Reinforced Learning • Unsupervised Learning – Clustering – use of K Means in numerical example • Bias & Variance , Overfitting and Underfitting Trees with reference to Training and Test Data • Ensemble Learning Technique using Bagging • Random Forest
- 4. How does id3 algorithm work? The ID3 algorithm begins with the original set as the root node. On each iteration of the algorithm, it iterates through every unused attribute of the set and calculates the entropy or the information gain of that attribute. ... The set is then split or partitioned by the selected attribute to produce subsets of the data.
- 5. What is id3 in decision tree? Very simply, ID3 builds a decision tree from a fixed set of examples. ... The leaf nodes of the decision tree contain the class name whereas a non-leaf node is a decision node. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute.
- 6. How does CART algorithm work? A Classification and Regression Tree(CART) is a predictive algorithm used in machine learning. It explains how a target variable's values can be predicted based on other values. It is a decision tree where each fork is a split in a predictor variable and each node at the end has a prediction for the target variable
- 7. What is CART technique? Decision Trees Classification and Regression Trees or CART for short is a term introduced by Leo Breiman to refer to Decision Tree algorithms that can be used for classification or regression predictive modeling problems.
- 8. What is a cart analysis? Classification and regression tree (CART) analysis recursively partitions observations in a matched data set, consisting of a categorical (for classification trees) or continuous (for regression trees) dependent (response) variable and one or more independent (explanatory) variables, into progressively smaller groups
- 9. What is Naive Bayes classifier algorithm? Naive Bayes classifiers are a collection of classification algorithms based on Bayes' Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.
- 10. What is K means clustering used for? Business Uses The K-means clustering algorithm is used to find groups which have not been explicitly labelled in the data. ... Once the algorithm has been run and the groups are defined, any new data can be easily assigned to the correct group. This is a versatile algorithm that can be used for any type of grouping.
- 12. What is K means clustering in data mining? Data mining with the K-Means algorithm The k-means clustering algorithm is a data mining and machine learning tool used to cluster observations into groups of related observations without any prior knowledge of those relationships. ... The term "k-means" was coined in 1967 by James McQueen
- 13. How is K means calculated? K-Means Clustering Select k points at random as cluster centers. Assign objects to their closest cluster center according to the Euclidean distance function. Calculate the centroid or mean of all objects in each cluster. Repeat steps 2, 3 and 4 until the same points are assigned to each cluster in consecutive rounds.
- 14. What is a supervised learning model? Supervised learning is a learning model built to make prediction, given an unforeseen input instance. A supervised learning algorithm takes a known set of input dataset and its known responses to the data (output) to learn the regression/classification model.
- 15. How does supervised machine learning work? Supervised machine learning builds a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data.
- 16. What is meant by unsupervised learning? Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.
- 17. How does unsupervised learning work? In unsupervised learning, an AI system is presented with unlabelled, uncategorized data and the system's algorithms act on the data without prior training. ... In essence, unsupervised learning can be thought of as learning without a teacher. In case of supervised learning, the system has both the inputs and the outputs.
- 20. What is meant by reinforcement learning? Reinforcement Learning is a type of Machine Learning, and thereby also a branch of Artificial Intelligence. It allows machines and software agents to automatically determine the ideal behaviour within a specific context, in order to maximize its performance.
- 22. What is reinforcement learning used for? Reinforcement learning is a type of Machine Learning algorithms which allows software agents and machines to automatically determine the ideal behaviour within a specific context, to maximize its performance. An extensive blog post, which you can read here - How businesses can leverage reinforcement learning?
- 23. What is bias and variance? Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance.
- 25. What is bias and variance in Supervised learning? The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set: The bias error is an error from erroneous assumptions in the learning algorithm.
- 26. What is bias and variance in Decision Trees? Q: Explain the bias vs. variance trade-off in statistical learning. A: The bias-variance trade-off is an important aspect of data science projects based on machine learning. ... The error due to squared bias is the amount by which the expected model prediction differs from the true value or target, over the training data.
- 27. What is Underfitting and Overfitting in Decision Trees ? Overfitting: Good performance on the training data, poor generalization to other data. Underfitting: Poor performance on the training data and poor generalization to other data.
- 29. What is meant by Overfitting of data? Overfitting is a modelling error which occurs when a function is too closely fit to a limited set of data points. Overfitting the model generally takes the form of making an overly complex model to explain idiosyncrasies in the data under study.
- 30. How do you deal with Underfitting? According to Andrew Ng, the best methods of dealing with an underfitting model is trying a bigger neural network (adding new layers or increasing the number of neurons in existing layers) or training the model a little bit longer.
- 31. What are the differences between Overfitting and Underfitting? Overfitting is a modelling error which occurs when a function is too closely fit to a limited set of data points. Underfitting refers to a model that can neither model the training data nor generalize to new data. ... Intuitively, underfitting occurs when the model or the algorithm does not fit the data well enough.
- 32. What is Ensemble Learning? Ensemble Learning is a process using which multiple machine learning models (such as classifiers) are strategically constructed to solve a particular problem. Let's take a real example to build the intuition. Suppose, you want to invest in a company XYZ. You are not sure about its performance though.
- 34. How do ensemble models work? What is an ensemble method? Ensemble models in machine learning combine the decisions from multiple models to improve the overall performance. They operate on the similar idea as employed while buying headphones. The main causes of error in learning models are due to noise, bias and variance.
- 35. Random Forest Model Random forests, otherwise known as the random forest model, is a method for classification and other tasks. It operates from decision trees and outputs classification of the individual trees. Random forests correct for the habit of decision trees to overfit to their training set.
- 37. What is test data and train data? The training data set is a grouped set of examples that are used to fit the parameters. The test data set is the last evaluation of the final model fit on the training data set. So like the last test before it's the real deal. Testing against each other ensures the machine learning model will be more accurate.
- 38. What is meant by test data? Test data is the data that is used in tests of a software system. ... When test data is entered the expected result should come and some test data is used to verify the software behaviour to invalid input data. Test data is generated by testers or by automation tools which support testing.