Classification error management based on cutoff threshold (log regression method)

2. A Machine Learning Specialist is training a logistic regression model for predicting hotel booking cancellations. The Specialist hopes to develop the best version of the model with the optimal classification threshold score. For this reason, he needs to evaluate how various threshold settings affect model performance. Which approach should the Specialist apply?Evaluate the model using recallEvaluate the model using the Root Mean Square Error (RMSE).Evaluate the model using the L1 norm.Evaluate the model using the Receiver Operating Characteristic Curve (ROC)Evaluate the model using a scatter plot graph.

Classification model errors

Which supervised learning algorithm is based on the idea of minimizing the cross-entropy loss between the predicted and actual target classes?Review LaterLinear regressionLogistic regressionRecallMean Squared Error

You need to evaluate a classification model. Which metric can you use? ​​Mean squared error (MSE)PrecisionSilhouette

Question 4You are working on an automated check-out kiosk for a supermarket, and are building a classifier for apples, bananas and oranges. Suppose your classifier obtains a training set error of 0.5%, and a dev set error of 7%. Which of the following are promising things to try to improve your classifier? (Check all that apply.)