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
Question
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
Solution
The supervised learning algorithm based on the idea of minimizing the cross-entropy loss between the predicted and actual target classes is Logistic Regression.
Here's a step-by-step explanation:
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Supervised Learning: This is a type of machine learning where the model is trained on a labelled dataset. That is, the correct answers (or targets) are provided to the model during training.
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Cross-Entropy Loss: This is a loss function that is used in classification problems. It measures the dissimilarity between the predicted probability distribution and the actual distribution. The goal is to minimize this loss.
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Logistic Regression: This is a statistical model used for binary classification problems. It uses the logistic function to model the probability of a certain class or event.
So, in the context of supervised learning, Logistic Regression uses the concept of cross-entropy loss to predict the probability of a certain class or event. By minimizing the cross-entropy loss, the model's predictions become more accurate.
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