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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