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B.Tech AKTU Quantum Book delves into the world of Machine Learning Techniques. Access critical notes, frequently asked questions, and essential insights for understanding this transformational field. **Unit-2 Regression and Bayesian Learning**

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**Q1. Describe briefly linear regression.**

**Ans. **1. A supervised machine learning approach called linear regression produces continuous outputs with constant slopes.

2. Rather than attempting to categorise values into groups, it is used to forecast values within a continuous range (for instance, sales or price) (for example: cat, dog).

3. Following are the types of linear regression:

**a. Simple regression : **

i. Simple linear regression uses traditional slope-intercept form to produce accurate prediction,

y = mx + b

where, m and b are the variables,

x represents our input data and y represents our prediction.

**b. Multivariable regression: **

i. A multi-variable linear equation is given below, where w represents the coefficients, or weights:

ii.The variables x, y, z represent the attributes, or distinct pieces of information that, we have about each observation.

iii. For sales predictions, these attributes might include a company’s advertising spend on radio, TV, and newspapers.

Sales = w_{1} Radio + w_{2} TV + w_{3} Newspapers

**Q2. Differentiate between linear regression and logistics regression.**

**Ans. **

S. No. | Linear regression | Logistics regression |

1. | A supervised regression model is linear regression. | A model of supervised classification is logistic regression. |

2. | In Linear regression, we predict the value by an integer number. | In Logistic regression, we predict the value by 1 or 0. |

3. | No activation function is used. | A logistic regression equation is created by converting a linear regression equation using an activation function. |

4. | A threshold value is added. | No threshold value is needed. |

5. | It is based on the least square estimation. | The dependent variable consists of only two categories. |

6. | In the event that the independent variables change, linear regression is employed to estimate the dependent variable. | Logistic regression is used to calculate the probability of an event. |

7. | Linear regression assumes the normal or gaussian distribution of the dependent variable. | Logistic regression assumes the binomial distribution of the dependent variable. |

**Q3. What are the parameters used in support vector classifier ? **

**Ans. Parameters used in support vector classifier are: **

**1. Kernel:**- a. The type of data and the type of transformation are taken into consideration while choosing the kernel.
- b. The kernel is a Radial Basis Function Kernel by default (RBF).

**2 Gamma:**- a. The influence of a single training sample during transformation is determined by this parameter, which in turn determines how closely the decision borders end up enclosing points in the input space.
- b. Points further apart are regarded as similar if gamma has a modest value.
- c. As a result, more points are grouped together and the decision boundaries are smoother (may be less accurate).
- d. Greater gamma values result in points being closer together (may cause overfitting).

**3. The ‘C’ parameter:**- a. This parameter regulates the degree of regularization that is done to the data.
- b. Low regularization, indicated by large values of C, results in excellent fit of the training data (may cause overfitting).
- c. More regularization, which results in a lower value of C, makes the model more error-tolerant (may lead to lower accuracy).

**Q4. Explain Bayesian network by taking an example. How is the Bayesian network powerful representation for uncertainty knowledge ?**

**Ans. **

- 1.
- 2. The full specification is as follows:
- i. A set of random variables makes up the nodes of the network variables may be discrete or continuous.
- ii. A set of directed links or arrows connects pairs of nodes. If there is an arrow from x to node y, x is said to be a parent of y.
- iii. Each node x
_{i}has a conditional probability distribution P(x_{i}|parent (x_{i})) that quantifies the effect of parents on the node. - iv. The graph has no directed cycles (and hence is a directed acyclic graph or DAG).

- 3. A Bayesian network offers an exhaustive account of the domain. The data in the network can be used to determine each entry in the whole joint probability distribution.
- 4. Bayesian networks give the field a clear approach to depict conditional independence relationships.
- 5. An exponentially smaller Bayesian network frequently exists than the total joint distribution.

**For example:**

- 1. Suppose we want to determine the possibility of grass getting wet or dry due to the occurrence of different seasons.
- 2. The weather has three states : Sunny, Cloudy, and Rainy. There are two possibilities for the grass: Wet or Dry.
- 3. The sprinkler can be on or off. Ifit is rainy, the grass gets wet but if it is sunny, we can make grass wet by pouring water from a sprinkler.
- 4. Suppose that the grass is wet. This could be contributed by one of the two reasons – Firstly, it is raining. Secondly, the sprinklers are turned on.
- 5. Using the Baye’s rule, we can deduce the most contributing factor towards the wet grass.

**Bayesian network possesses the following merits in uncertainty knowledge representation: **

- 1. The Bayesian network can easily accommodate missing data.
- 2. A Bayesian network can pick up on a variable’s haphazard relationship. In data analysis, a casual relationship is beneficial for comprehending field knowledge and can readily result in precise prediction even in the presence of significant interference.
- 3. Field knowledge and data-based information can be fully utilised when a bayesian network and bayesian statistics are combined.
- 4. Combining the Bayesian network with different models can successfully prevent the over-fitting issue.

**Q5. Describe the usage, advantages and disadvantages of EM algorithm. **

**Ans. Usage of EM algorithm:**

- 1. It can be used to complete the gaps in a sample’s data.
- 2. It can serve as the foundation for cluster learning that is unsupervised.
- 3. It can be utilised to estimate the Hidden Markov Model’s parameters (HMM).
- 4. lt can be used to determine latent variable values.

**Advantages of EM algorithm are:**

- 1. With each iteration, the likelihood is always certain to rise.
- 2. In terms of implementation, the E-step and M-step are frequently quite simple for many issues.
- 3. The closed form of solutions to the M-steps is frequently seen.

**Disadvantages of EM algorithm are:**

- 1. It has slow convergence.
- 2. It makes convergence to the local optima only.
- 3. It needs both the forward and backward probabilities (numerical optimization requires only forward probability).

**Q6. What are the advantages and disadvantages of SVM ? **

**Ans. Advantages of SVM are: **

**1. Guaranteed optimality:**Owing to the nature of Convex Optimization, the solution will always be global minimum, not a local minimum.**2. The abundance of implementations:**We can access it conveniently.- 3. Both linearly and non-linearly separable data can be used with SVM. Whereas non-linearly separable data presents a soft margin, linearly separable data crosses the hard margin.
- 4. SVMs offer semi-supervised learning models conformance. It can be applied to both labelled and unlabeled data sets. The transductive SVM is just one of the conditions needed to solve the minimization problem.
- 5. Feature mapping used to place a significant burden on the computational complexity of the model’s overall training efficiency. But, SVM may perform the feature mapping using the straightforward dot product with the aid of Kernel Trick.

**Disadvantages of SVM: **

- 1. As compared to other text data handling techniques, SVM does not provide the best performance when handling text structures. This causes a loss of sequential information, which affects performance.
- 2. Unlike logistic regression, SVM cannot provide the probabilistic confidence value. Due to the importance of prediction certainty in many applications, this doesn’t really explain anything.
- 3. The support vector machine’s major drawback may be the kernel selection. It becomes challenging to select the best kernel for the data because there are so many of them available.

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