The          of the hyperplane depends upon the number of features.

Related MCQs

• 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)?
• The _____of the hyperplane depends upon the number of features.
• 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.
• The selling price of a house depends on many factors. For example, it depends on the number of bedrooms, number of kitchen, number of bathrooms, the year the house was built, and the square footage of the lot. Given these factors, predicting the selling price of the house is an example of ____________ task.
• Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features
• Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features
•            dataset with many features contains information proportional to the independence of all features and their variance.
• ______dataset with many features contains information proportional to the independence of all features and their variance.
• The effectiveness of an SVM depends upon: