Support Vector Machine is

Related MCQs

• The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N � the number of features) that distinctly classifies the data points.
• The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N — the number of features) that distinctly classifies the data points.
• Suppose we train a hard-margin linear SVM on n > 100 data points in R2, yielding a hyperplane with exactly 2 support vectors. If we add one more data point and retrain the classifier, what is the maximum possible number of support vectors for the new hyperplane (assuming the n + 1 points are linearly separable)?
• Machine learning techniques differ from statistical techniques in that machine learning methods
• Which of the following are correct statement(s) about stacking? 1. A machine learning model is trained on predictions of multiple machine learning models 2. A Logistic regression will definitely work better in the second stage as compared to other classification methods 3. First stage models are trained on full / partial feature space of training data
• Which of the following are correct statement(s) about stacking? A machine learning model is trained on predictions of multiple machine learning models A Logistic regression will definitely work better in the second stage as compared to other classification methods First stage models are trained on full / partial feature space of training data
• Machine learning techniques differ from statistical techniques in that machine learning methods
• In which of the following each categorical label is first turned into a positive integer and then transformed into a vector where only one feature is 1 while all the others are 0.
• What are support vectors?
• Let S1 and S2 be the set of support vectors and w1 and w2 be the learnt weight vectors for a linearly separable problem using hard and soft margin linear SVMs respectively. Which of the following are correct?