91. How can you avoid overfitting ?
92. What are the steps for using a gradient descent algorithm?
1. Calculate error between the actual value and the predicted value
2. Reiterate until you find the best weights of network
3. Pass an input through the network and get values from output layer
4. Initialize random weight and bias
5. Go to each neurons which contributes to the error and change its respective values to reduce the error
1. Calculate error between the actual value and the predicted value
2. Reiterate until you find the best weights of network
3. Pass an input through the network and get values from output layer
4. Initialize random weight and bias
5. Go to each neurons which contributes to the error and change its respective values to reduce the error
93. Problem in multi regression is ?
94. Tree/Rule based classification algorithms generate ... rule to perform the classification.
95. In the regression equation Y = 75.65 + 0.50X, the intercept is
96. there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an approach also allows simpler algorithms called . . . . . . . .
97. We can also compute the coefficient of linear regression with the help of an analytical method called "Normal Equation". Which of the following is/are true about "Normal Equation"?
1. We don't have to choose the learning rate
2. It becomes slow when number of features is very large
3. No need to iterate
1. We don't have to choose the learning rate
2. It becomes slow when number of features is very large
3. No need to iterate
98. Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
99. if there is only a discrete number of possible outcomes (called categories), the process becomes a . . . . . . . .
100. Give the correct Answer for following statements. 1. It is important to perform feature normalization before using the Gaussian kernel. 2. The maximum value of the Gaussian kernel is 1.
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