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