## 91. Which of the following assumptions do we make while deriving linear regression parameters?

1. The true relationship between dependent y and predictor x is linear

2. The model errors are statistically independent

3. The errors are normally distributed with a 0 mean and constant standard deviation

4. The predictor x is non-stochastic and is measured error-free

1. The true relationship between dependent y and predictor x is linear

2. The model errors are statistically independent

3. The errors are normally distributed with a 0 mean and constant standard deviation

4. The predictor x is non-stochastic and is measured error-free

## 92. For the given weather data, Calculate probability of not playing

## 93. In PCA the number of input dimensiona are equal to principal components

## 94. What are the two methods used for the calibration in Supervised Learning?

## 95. A student Grade is a variable F1 which takes a value from A,B,C and D. Which of the following is True in the following case?

## 96. Regression trees are often used to model . . . . . . . . data.

## 97. Support Vector Machine is

## 98. Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it's hyper parameter.What would happen when you use very large value of C(C->infinity)?

## 99. In SVR we try to fit the error within a certain threshold.

## 100. Which of the following are correct statement(s) about stacking?

1. A machine learning model is trained on predictions of multiple machine learning models

2. A Logistic regression will definitely work better in the second stage as compared to other classification methods

3. First stage models are trained on full / partial feature space of training data.

1. A machine learning model is trained on predictions of multiple machine learning models

2. A Logistic regression will definitely work better in the second stage as compared to other classification methods

3. First stage models are trained on full / partial feature space of training data.

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