In sklearn's Logistic Regression classifier, the parameter 'C' is used for regularization. Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function.

1. C is the inverse of regularization strength. This means a smaller value of C specifies stronger regularization.

2. For a very large value of C, the power of regularization will be very low. This means the model will try to fit the training data as well as possible, without much regard for creating a more generalized model.

3. This can lead to overfitting, where the model performs very well on the training data but poorly on unseen data.

4. Therefore, for a very large value of C, the Logistic Regression classifier may overfit the training data.

So, the correct statement would be "For a very large value of C, the Logistic Regression classifier may overfit the training data."

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In sklearn's Logistic Regression classifier, the parameter 'C' is used for regularization. Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function.

1. C is the inverse of regularization strength. This means a smaller value of C specifies stronger regularization.

2. For a very large value of C, the power of regularization will be very low. This means the model will try to fit the training data as well as possible, without much regard for creating a more generalized model.

3. This can lead to overfitting, where the model performs very well on the training data but poorly on unseen data.

4. Therefore, for a very large value of C, the Logistic Regression classifier may overfit the training data.

So, the correct statement would be "For a very large value of C, the Logistic Regression classifier may overfit the training data."

Knowee AI · Accepted Answer

In sklearn's Logistic Regression classifier, the parameter 'C' is used for regularization. Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function.

1. C is the inverse of regularization strength. This means a smaller value of C specifies stronger regularization.

2. For a very large value of C, the power of regularization will be very low. This means the model will try to fit the training data as well as possible, without much regard for creating a more generalized model.

3. This can lead to overfitting, where the model performs very well on the training data but poorly on unseen data.

4. Therefore, for a very large value of C, the Logistic Regression classifier may overfit the training data.

So, the correct statement would be "For a very large value of C, the Logistic Regression classifier may overfit the training data."

In sklearn Logistic Regression classifier for a very large value of C, choose the correct statement(s)

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