You need to identify numerical values that represent the probability of humans developing diabetes based on age and body fat percentage.
Which type of machine learning model should you use? - answer-Multiple linear regression
It models a relationship between two or more features and a single ...
Azure AI-900 Exam Example Questions
You need to identify numerical values that represent the probability of
humans developing diabetes based on age and body fat percentage.
Which type of machine learning model should you use? - answer-Multiple
linear regression
It models a relationship between two or more features and a single label,
which matches the scenario in this item.
Linear regression uses a single feature.
Logistic regression is a type of classification model, which returns either a
Boolean value or a categorical decision.
Hierarchical clustering groups data points that have similar characteristics.
Which type of machine learning algorithm groups observations is based on
the similarities of features? - answer-Clustering
Clustering algorithms group data points that have similar characteristics.
Regression algorithms are used to predict numeric values. Classification
algorithms are used to predict a predefined category to which an input value
belongs. Supervised learning is a category of learning algorithms that
includes regression and classification, but not clustering.
Which type of machine learning algorithm assigns items to a set of
predefined categories? - answer-Classification
Classification algorithms are used to predict a predefined category to which
an input value belongs. Regression algorithms are used to predict numeric
values. Clustering algorithms group data points that have similar
characteristics. Unsupervised learning is a category of learning algorithms
that includes clustering, but not regression or classification.
An electricity utility company wants to develop a mobile app for its
customers to monitor their energy use and to display their predicted energy
use for the next 12 months. The company wants to use machine learning to
provide a reasonably accurate prediction of future energy use by using the
customers' previous energy-use data.
Which type of machine learning is this? - answer-Regression
,Regression is a machine-learning scenario that is used to predict numeric
values. In this example, regression will be able to predict future energy
consumption based on analyzing historical time-series energy data based on
factors, such as seasonal weather and holiday periods. Multiclass
classification is used to predict categories of data. Clustering analyses
unlabeled data to find similarities present in the data. Classification is used
to predict categories of data.
You plan to use machine learning to predict the probability of humans
developing diabetes based on their age and body fat percentage.
What should the model include?
- Three features
- Three labels
- Two features and one label
- Two labels and one feature - answer-Two features and one label
The scenario represents a model that is meant to establish a relationship
between two features (age and body fat percentage) and one label (the
likelihood of developing diabetes). The features are descriptive attributes
(serving as the input), while the label is the characteristic you are trying to
predict (serving as the output).
In a regression machine learning algorithm, how are features and labels
handled in a validation dataset?
1. Features are compared to the feature values in a training dataset.
2. Features are used to generate predictions for the label, which is compared
to the actual label values.
This answer is correct.
3. Labels are compared to the label values in a training dataset.
4. The label is used to generate predictions for features, which are compared
to the actual feature values. - answer-2.
In a regression machine learning algorithm, features are used to generate
predictions for the label, which is compared to the actual label value. There
is no direct comparison of features or labels between the validation and
training datasets.
Which assumption of the multiple linear regression model should be satisfied
to avoid misleading predictions?
1. Features are dependent on each other.
2. Features are independent of each other.
, 3. Labels are dependent on each other.
4. Labels are independent of each other. - answer-2. Features are
independent of each other.
Multiple linear regression models the relationship between several features
and a single label. The features must be independent of each other,
otherwise, the model's predictions will be misleading.
Which feature makes regression an example of supervised machine
learning? - answer-Use of historical data with known label values to train a
model.
Regression is an example of supervised machine learning due to the use of
historical data with known label values to train a model. Regression does not
rely on randomly generated data for training.
In a regression machine learning algorithm, what are the characteristics of
features and labels in a training dataset? - answer-Known feature and label
values
In a regression machine learning algorithm, a training set contains known
feature and label values.
A company is using machine learning to predict house prices based on
appropriate house attributes.
For the machine learning model, which attribute is the label?
- Age of the house
- Floor space size
- Number of bedrooms
- Price of the house - answer-The price of the house is the label you are
attempting to predict through the machine learning model. This is typically
done by using a regression model. Floor space size, number of bedrooms,
and age of the house are all input variables for the model to help predict the
house price label.
A company is using machine learning to predict various aspects of its e-
scooter hire service dependent on weather. This includes predicting the
number of hires, the average distance traveled, and the impact on e-scooter
battery levels.
For the machine learning model, which two attributes are the features?
- distance traveled
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