- What are the differences between supervised and unsupervised learning ? Explain with suitable example.
- Explain the k-NN algorithm with suitable steps. How is the similarity between nearest neighbors estimated ?
- Given the following data. Find the class labels of test vectors using the 3-NN method. Show the output at each step of algorithm.
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| Input Data Matrix | Test Vectors |
|---|---|
| F1 F2 F3 F4 Labels 5.1 3.5 1.4 0.2 1 4.9 3.0 1.4 0.2 1 4.7 3.2 1.3 0.2 1 4.6 3.1 1.5 0.2 1 7.0 3.2 4.7 1.4 2 6.4 3.2 4.5 1.5 2 6.9 3.1 4.9 1.5 2 5.5 2.3 4.0 1.3 2 6.3 3.3 6.0 2.5 3 5.8 2.7 5.1 1.9 3 7.1 3.0 5.9 2.1 3 6.3 2.9 5.6 1.8 3 | F1 F2 F3 F4 Labels 6.30 2.92 5.80 2.04 ? 6.06 3.25 3.89 1.25 ? 5.14 3.66 2.86 0.46 ? Fx : Features Labels: Class Labels Calculate: Distance Vector Matrix Class labels of test vectors |
4. Explain how the logistic regression is different from multi-linear regression.
5. Derive the expression of parameter update using gradient descent for multilinear regression.
6. Derive the equation of decision boundary for logistic regression.