CertNexus Certified Artificial Intelligence Practitioner
Last Update Oct 2, 2023
Total Questions : 90
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Which of the following is a common negative side effect of not using regularization?
Overfitting is a common negative side effect of not using regularization. Regularization is a technique that reduces the complexity of a model by adding a penalty term to the loss function, which prevents the model from learning too many parameters that may fit the noise in the training data. Overfitting occurs when the model performs well on the training data but poorly on the test data or new data, because it has memorized the training data and cannot generalize well. References: Regularization (mathematics) - Wikipedia, Overfitting in Machine Learning: What It Is and How to Prevent It
Which of the following text vectorization methods is appropriate and correctly defined for an English-to-Spanish translation machine?
Text vectorization is a technique that converts text into numerical vectors that can be used by machine learning models. Text vectorization can use different methods to represent text features, such as word frequency, word order, word meaning, or word context. Some of the common text vectorization methods are:
For an English-to-Spanish translation machine, using Word2vec would be appropriate and correctly defined, because in translation machines, we need to consider the order of the words, as well as their meaning and context.
Which of the following is TRUE about SVM models?
SVM models can use kernel functions to map the input data into higher-dimensional feature spaces, where linear separation is possible. This allows SVM models to handle non-linear problems effectively. References: CertNexus Certified Artificial Intelligence Practitioner, Support vector machine - Wikipedia