Logistic Regression, SVM Quiz

What do you mean by a hard margin?

• The SVM allows a very low error in classification

• The SVM allows a high amount of error in the classification

• None of the above

The effectiveness of an SVM depends upon _____________.

• Selection of Kernel trick

• Kernel Parameters

• Soft Margin Parameter C

• All of the above

Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?

• AUC-ROC

• Accuracy

• Logloss

• Mean-Squared-Error

Which of the following algorithms do we use for Feature/Variable Selection?

• LASSO

• Ridge

• Both

• None of these

If I am using all features of my dataset and I achieve 100% accuracy on my training data set but ~70% on the validation set of data, what should I look out for?

• Underfitting

• Nothing; the model is perfect

• Overfitting

In logistic regression, what does the sigmoid function output?

• A continuous value between 0 and 1

• A binary classification

• A probability distribution

• A linear value

What does a low F1 score indicate in a model's performance?

• The model has a high number of false positives.

• The model has a high number of false negatives.

• The model has a good balance between precision and recall.

• The model is overfitting the data.

If you want to focus on minimizing false negatives in your model, which metric would you optimize for?

• Precision

• Recall

• Accuracy

• Specificity

What is the primary goal of a Support Vector Machine (SVM) classifier?

• To minimize the distance between data points and the decision boundary

• To maximize the margin between the decision boundary and the nearest data points of any class

• To minimize the classification error for all data points

• To maximize the likelihood function of the dataset

Which of the following is true about the support vectors in an SVM?

• They are the data points that lie on the decision boundary.

• They are the data points farthest from the decision boundary.

• They are the data points that influence the orientation and position of the decision boundary.

• They are the data points that do not influence the classification decision.