Practice More Quizzes:

## Q:

### (True/False) A linear classifier can only learn positive coefficients.

## Q:

### (True/False) In order to train a logistic regression model, we find the weights that maximize the likelihood of the model.

## Q:

### (True/False) The data likelihood is the product of the probability of the inputs mathbf{x}x given the weights mathbf{w}w and response yy.

## Q:

### Questions 4 and 5 refer to the following scenario. Consider the setting where our inputs are 1-dimensional. We have data xx yy 2.5 +1 0.3 -1 2.8 +1 0.5 +1 and the current estimates of the weights are w_0 = 0w0=0 and w_1 = 1w1=1. (w_0w0: the intercept, w_1w1: the weight for xx).Calculate the likelihood of this data. Round your answer to 2 decimal places.

## Q:

### Refer to the scenario given in Question 4 to answer the following: Calculate the derivative of the log likelihood with respect to w1w_1w1. Round your answer to 2 decimal places.

## Q:

### Which of the following is true about gradient ascent? Select all that apply.

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