what's the cost function of the logistic regression mcq

Xp is linear. b) Logistic regression. . Select the option (s) which is/are correct in such a case. Hence, the log odds become: ln (P1P) = 0.47 X1 0.45 X2+0.39 X30.23 X4+0.55 X5 As you can see, we have ignored the 0 since it will be the same for all the three consumers. Now, you want to add a few new features in the same data. Answer: a. Clarification: Linear regression is a simple approach to supervised learning. Cross-entropy or log loss is used as a cost function for logistic regression. But this results in cost function with local optima's which is a very big problem for Gradient Descent to compute the global optima. In the cost function for logistic regression, the confident wrong predictions are penalised heavily. During training: We need to store four things in memory: x, y, w, and b during training a Logistic Regression model. a) Linear regression. . Let's now imagine that we apply to this logistic model the same error function that's typical for linear regression models. We can write as: 4.2. Due to this reason, MSE is not suitable for logistic regression. Answer: D. Explanation: All of the above are are the advantages of Logistic Regression. Finding the global minimum in such cases using gradient descent is not possible. 14. x and y are two matrices of dimension (n x d) and (n x 1) respectively. The confident right predictions are rewarded less. Squaring this non-linear transformation will lead to non-convexity with local minimums. In the case of Linear Regression, the Cost function is - But for Logistic Regression, It will result in a non-convex cost function. Logistic regression predicts the output of a categorical dependent variable. So, for Logistic Regression the cost function is If y = 1 Why cant we use Mean Square Error (MSE) as a cost function for logistic regression. There should be a linear relationship between the logit of the outcome and each. Logistic Regression Interview Questions The logistic regression assumes that there is minimal or no multicollinearity among the independent variables. In the Logistic regression model the value of classier lies between 0 to 1. d) Greedy algorithms. 7. Which of the following function is used by logistic regression to convert the probability in the range between [0,1]. D. All of the above. c) The cost function of logistic regression is concave d) The cost function of logistic regression is convex Answer: (d) The cost function of logistic regression is convex Gradient descent will converge into global minimum only if the cost function is convex in the case of logistic regression. This function normally consists of the mean squared error () between the model's predictions and the values of the target variable. It assumes that the dependence of Y on X1, X2, . 19) Suppose, You applied a Logistic Regression model on a given data and got a training accuracy X and testing accuracy Y. In logistic regression, we use the sigmoid function and perform a non-linear transformation to obtain the probabilities. By optimising this cost function, convergence is achieved. linear regression is an incredibly powerful tool for analysing data. In the cost function for logistic regression, the confident wrong predictions are penalised heavily. Logistic regression cost function For logistic regression, the C o s t function is defined as: C o s t ( h ( x), y) = { log ( h ( x)) if y = 1 log ( 1 h ( x)) if y = 0 The Problem of Convexity A. Logistic Regression is very easy to understand. By optimising this cost function, convergence is achieved. Well, it turns out that for logistic regression we just have to find a different C o s t function, while the summation part stays the same. Here, there are five variables for which the coefficients are given. It is used for predicting the categorical dependent variable using a given set of independent variables. Learn what is Logistic Regression Cost Function in Machine Learning and the interpretation behind it. Finding the global minimum in such cases using gradient descent is not possible. The confident right predictions are rewarded less. Model will become very simple so bias will be very high. Now, using the values of the 5 variables given, you get - c) Gradient Descent. So let's fit the. C. It performs well for simple datasets as well as when the data set is linearly separable. In the cost function for logistic regression, the confident wrong predictions are penalised heavily. Logistic Regression Cost function is "error" representa. It can be either Yes or No, 0 or 1, true or False, etc. Solution: A. Cross-entropy or log loss is used as a cost function for logistic regression. Therefore the outcome must be a categorical or discrete value. Discuss the space complexity of Logistic Regression. Cross-entropy or log loss is used as a cost function for logistic regression. but instead of giving the exact value as 0 . Storing b is just 1 step, i.e, O (1) operation since b is a constant. 6 Logistic Regression transforms the output probability to be in a range of [0, 1]. A Sigmoid B Mode C Square D All of the above 7 Which of the following method (s) does not have closed form solution for its coefficients? It requires less training. B. Due to this reason, MSE is not suitable for logistic regression. 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