CertNexus AIP-210 Dumps PDF Free Download - Page: 10

QUESTION: 11

A classifier has been implemented to predict whether or not someone has a specific type of disease. Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?

Mean squared error
Precision and accuracy
Precision and recall
Recall and explained variance

Answer(s): C

Explanation:

Precision and recall are two measures that can evaluate the performance of a classifier, especially when the data is imbalanced. Precision is the ratio of true positives (correctly predicted positive cases) to all predicted positive cases. Recall is the ratio of true positives to all actual positive cases. Precision and recall can help assess how well the classifier can identify the positive cases (the disease) and avoid false negatives (missed diagnosis) or false positives (unnecessary treatment).

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QUESTION: 12

Which of the following describes a typical use case of video tracking?

Augmented dreaming
Medical diagnosis
Traffic monitoring
Video composition

Answer(s): C

Explanation:

Video tracking is a technique that involves detecting and following moving objects in a video sequence. Video tracking can be used for various applications, such as surveillance, security, sports analysis, and human-computer interaction. One typical use case of video tracking is traffic monitoring, where video tracking can help measure traffic flow, detect congestion, identify violations, and optimize traffic signals.

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QUESTION: 13

You are developing a prediction model. Your team indicates they need an algorithm that is fast and requires low memory and low processing power. Assuming the following algorithms have similar accuracy on your data, which is most likely to be an ideal choice for the job?

Deep learning neural network
Random forest
Ridge regression
Support-vector machine

Answer(s): C

Explanation:

Ridge regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Ridge regression is fast and requires low memory and low processing power, as it only involves solving a system of linear equations. Ridge regression can also handle multicollinearity (high correlation among predictors) by shrinking the coefficients of correlated predictors.

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QUESTION: 14

For each of the last 10 years, your team has been collecting data from a group of subjects, including their age and numerous biomarkers collected from blood samples. You are tasked with creating a prediction model of age using the biomarkers as input. You start by performing a linear regression using all of the data over the 10-year period, with age as the dependent variable and the biomarkers as predictors.
Which assumption of linear regression is being violated?

Equality of variance (Homoscedastidty)
Independence
Linearity
Normality

Answer(s): B

Explanation:

Independence is an assumption of linear regression that states that the errors (residuals) of the model are independent of each other, meaning that they are not correlated or influenced by previous or subsequent errors. Independence can be violated when the data has serial correlation or autocorrelation, which means that the value of a variable at a given time depends on its previous or future values. This can happen when the data is collected over time (time series) or over space (spatial data). In this case, the data is collected over time from a group of subjects, which may introduce serial correlation among the errors.

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QUESTION: 15

When should you use semi-supervised learning? (Select two.)

A small set of labeled data is available but not representative of the entire distribution.
A small set of labeled data is biased toward one class.
Labeling data is challenging and expensive.
There is a large amount of labeled data to be used for predictions.
There is a large amount of unlabeled data to be used for predictions.

Answer(s): C,E

Explanation:

Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. Semi-supervised learning can be useful when:
Labeling data is challenging and expensive: Labeling data requires human intervention and domain expertise, which can be costly and time-consuming. Semi-supervised learning can leverage the large amount of unlabeled data that is easier and cheaper to obtain and use it to improve the model's performance.
There is a large amount of unlabeled data to be used for predictions: Unlabeled data can provide additional information and diversity to the model, which can help it learn more complex patterns and generalize better to new data. Semi-supervised learning can use various techniques, such as self- training, co-training, or generative models, to incorporate unlabeled data into the learning process.

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CertNexus Certified Artificial Intelligence Practitioner AIP-210 Dumps in PDF

Free CertNexus AIP-210 Real Questions (page: 10)

QUESTION: 11

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QUESTION: 13

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QUESTION: 14

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QUESTION: 15

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