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(a) Logistic regression is a supervised machine learning algorithm. True False (b) Logistic regression is mainly used for regression problems. True False (c) Mean squared error (MSE) is used to measure the goodness of fit for logistic regression. True False

(a) Logistic regression is a supervised machine learning algorithm. True False (b) Logistic regression is mainly used for regression problems. True False (c) Mean squared error (MSE) is used to measure the goodness of fit for logistic regression. True False

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(a) Logistic regression is a supervised machine learning algorithm. True False (b) Logistic regression is mainly used for regression problems. True False (c) Mean squared error (MSE) is used to measure the goodness of fit for logistic regression. True False (d) When the hypothesis space becomes richer (i.e., more degrees of freedom in the model), over- fitting is more likely for a given data set. True False (e) The number of epochs in gradient descent refers to the size of each minibatch. True False (f) Both bias and variance decrease as the model complexity increases. True False (g) A classifier that attains 100% accuracy on the training set and 70% accuracy on the test set is better than a classifier that attains 70% accuracy on the training set and 75% accuracy on the test set. True False (h) The cross-entropy loss can be used for regression problems to avoid overfitting. True False
Transcribed Image Text:(a) Logistic regression is a supervised machine learning algorithm. True False (b) Logistic regression is mainly used for regression problems. True False (c) Mean squared error (MSE) is used to measure the goodness of fit for logistic regression. True False (d) When the hypothesis space becomes richer (i.e., more degrees of freedom in the model), over- fitting is more likely for a given data set. True False (e) The number of epochs in gradient descent refers to the size of each minibatch. True False (f) Both bias and variance decrease as the model complexity increases. True False (g) A classifier that attains 100% accuracy on the training set and 70% accuracy on the test set is better than a classifier that attains 70% accuracy on the training set and 75% accuracy on the test set. True False (h) The cross-entropy loss can be used for regression problems to avoid overfitting. True False
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