Question 1You train a Support Vector Machine and obtain an accuracy of 100% on the training data and 50% on the validation data. This is an example of:1 pointOverfitting Underfitting A good model
Question
Question 1You train a Support Vector Machine and obtain an accuracy of 100% on the training data and 50% on the validation data. This is an example of:1 pointOverfitting Underfitting A good model
Solution
This is an example of Overfitting.
Overfitting occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. The problem is that these concepts do not apply to new data and negatively impact the model's ability to generalize.
In this case, the model has learned the training data perfectly (100% accuracy) but performs poorly on the validation data (50% accuracy). This large discrepancy in performance indicates that the model is not generalizing well to unseen data, which is a clear sign of overfitting.
Similar Questions
What is a support vector in the context of support vector machines?Question 7Answera.A data point that is used to define the decision boundaryb.A data point that lies on the margin boundaryc.A data point that maximizes the margind.A data point that is classified correctly
What is the objective of a Support Vector Machine (SVM)?Answer areaTo maximize the distance between the decision boundary and the nearest data points of any classTo minimize the number of misclassified pointsTo maximize the number of support vectorsTo minimize the computational complexity
What is a support vector machine?Question 2Answera.A linear model used to make predictions in supervised learningb.A neural network used in deep learningc.A tree-like model used to make predictions in supervised learningd.A clustering algorithm used in unsupervised learning
What is a support vector regression machine?Question 1Answera.A support vector machine that is sensitive to the presence of outliersb.A support vector machine that is sensitive to the scale of the input variablesc.A support vector machine that is used for regressiond.A support vector machine that is used for classification
hat are Support Vector Machines (SVMs)?1 pointSVMs generate support vector points in order to minimize the loss function.SVMs attempts to bring support vector points closer together.SVMs maximally separates the closest support vector points.SVMs remove support vector points in order to minimize the loss function.
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