Data Warehousing and Data Mining · Unit 6 · 10 hrs
Classification and Prediction
Exam-focused notes for Classification and Prediction (Data Warehousing and Data Mining, CSC420): what the TU syllabus asks and how it has actually been tested, with 11 solved past questions from this unit.
What this unit covers
- Definition (Classification, Prediction)
- Learning and testing of classification
- Classification by decision tree induction
- ID3 as attribute selection algorithm
- Bayesian classification
- Laplace smoothing
- Classification by backpropagation
- Rule based classifier (Decision tree to rules, rule coverage and accuracy, efficient of rule simplification)
- Support vector machine
- Evaluating accuracy (precision, recall, f-measure)
- Issues in classification
- Overfitting and underfitting
- K-fold cross validation
- Comparing two classifier (McNemar's test)
ID3 as attribute selection algorithm
Decision Tree Classification with ID3 Algorithm
TID Age Car Type Class ---------------------------- 1 ≤30 Family High 2 ≤30 Sports High 3 30 Sports High 4 30 Family Low 5 30 Truck Low 6 ≤30 Family High - Total tuples = 6 - Class High: TID 1, 2, 3, 6 → 4 tuples - Class Low: TID 4, 5 → 2 tuples - Attribute...
Full solved answer →How Classification Differs from Regression. Train ID3 Classifier
Aspect Classification Regression --------- Output Discrete class label (e.g. Up/Down) Continuous numeric value Goal Assign a tuple to a predefined category Predict a numeric quantity Example Loan = "safe"/"risky" Predicting house price Algorithms ID3, Naive...
Full solved answer →Support vector machine
What is support vector? How do you evaluate the accuracy of a classifier? Describe. [5]
--- A Support Vector Machine (SVM) is a supervised learning algorithm used primarily for classification problems. It works by finding the optimal hyperplane that best separates data points belonging to different classes. Support Vectors are the data points ...
Full solved answer →Write down short notes on: a. Support Vector Machine b. Multi-dimensional Data Model [5]
--- Support Vector Machine (SVM) is one of the most popular supervised learning algorithms, used primarily for classification problems, though it can also be applied to regression tasks in machine learning. - Working Principle: SVM takes input data points a...
Full solved answer →Classification by backpropagation
Which algorithm is used for training multi-layer perceptron? Discuss the algorithm in detail. [5]
The Backpropagation Algorithm is used for training multi-layer perceptrons (MLP). It is the most popular and widely used method for training multilayer neural networks. --- The training in backpropagation proceeds in two phases: 1. Forward Phase (Forward Pa...
Full solved answer →When Multilayer Perceptron is Better Choice Over Other Classification Algorithms
Consider a multilayer feed-forward neural network given below. Let the learning rate be 0.5. Assume initial values of weights and biases as given in the table below. Train the network for the training tuples (1, 1, 0) and (0, 1, 1), where last number is target output. Show weight and bias updates by using back-propagation algorithm. Assume that sigmoid activation function is used in the network.
$$\begin{array}{c|c|c|c|c|c|c|c|c} w_{13} & w_{14} & w_{23} & w_{24} & w_{35} & w_{45} & b_3 & b_4 & b_5 \ \hline 0.5 & 0.2 & -0.3 & 0.5 & 0.1 & 0.3 & 0.6 & -0.4 & 0.8 \ \end{array}$$
[10]
- Inputs: node 1 ($x1$), node 2 ($x2$); Hidden: nodes 3, 4; Output: node 5 - Weights: $w{13}=0.5,\ w{14}=0.2,\ w{23}=-0.3,\ w{24}=0.5,\ w{35}=0.1,\ w{45}=0.3$ - Biases: $b3=0.6,\ b4=-0.4,\ b5=0.8$ - Learning rate: $\alpha=0.5$ - Activation: sigmoid $\sigma(...
Full solved answer →Evaluating accuracy
What is confusion matrix? Discuss various classification measures along with their mathematical formulae. [5]
A Confusion Matrix is an N×N matrix used for evaluating the performance of a classification model, where N is the number of target classes. For a binary classification problem, we have a 2×2 matrix: Predicted: Positive Predicted: Negative --------- Actual: ...
Full solved answer →Bayesian classification
Consider the following data set. Find out whether the object with attribute Confident = Yes, Sick = No will Fail or Pass using Bayesian classification.
| Confident | Studied | Sick | Result |
|---|---|---|---|
| Yes | No | No | Fail |
| Yes | No | No | Pass |
| No | Yes | Yes | Fail |
| No | Yes | Yes | Pass |
| Yes | Yes | Yes | Pass |
[5]
Row Confident Studied Sick Result --------------------------------------- 1 Yes No No Fail 2 Yes No No Pass 3 No Yes Yes Fail 4 No Yes Yes Pass 5 Yes Yes Yes Pass Query object: Confident = Yes, Sick = No (Studied not specified). Total instances = 5. - Fail:...
Full solved answer →K-fold cross validation
How do you evaluate the accuracy of a classifier? Discuss the advantages of using K-fold cross validation. [5]
The accuracy of a classifier measures how well it correctly predicts the class label of unseen tuples. The common methods are: - The dataset D is randomly partitioned into two independent sets: - Training set (typically 2/3 of data): used to build the class...
Full solved answer →Definition
How classification plays significance role in data mining? Explain. [5]
Classification is a supervised data mining technique used to predict the class label (category) of unknown data objects based on a trained model. It maps input data into predefined classes or categories by learning patterns from labeled training data. --- 1...
Full solved answer →Classification by decision tree induction
Write short notes on (Any Two): a. Evolution analysis b. Decision trees c. Text mining d.Classification using Regression [5]
--- Evolution analysis refers to the description and modeling of regularities or trends for objects whose behavior changes over time. - It is one of the core functional modules found in the Data Mining Engine, which is the central component of a data mining...
Full solved answer →Make Unit 6 stick
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