HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
Answer(s): A
Box: Evaluation results output featureIn Azure Machine Learning designer, you use the ____________ to explore distribution of values in potential feature columns.In Azure Machine Learning, the "evaluation results output feature" refers to the ability to generate and view detailed metrics and insights on the performance of your models, prompt flows, and other machine learning components. This feature allows you to understand how well your models are performing on a dataset, identify areas for improvement, and compare different models or configurations.The "Data Preprocessor" component, specifically its "Data Profile" feature, is used to explore the distribution of values in potential feature columns. This component provides in-line visualizations like value counts (for strings, booleans, dates) and histograms (for decimals/numerics) to quickly understand data distributions.See note below.Incorrect:* Normalize Data moduleUse this component to transform a dataset through normalization.Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to use a common scale, without distorting differences in the ranges of values or losing information. Normalization is also required for some algorithms to model the data correctly.* Select Columns in Dataset moduleUse this component to choose a subset of columns to use in downstream operations. The component does not physically remove the columns from the source dataset; instead, it creates a subset of columns, much like a database view or projection.This component is useful when you need to limit the columns available for a downstream operation, or if you want to reduce the size of the dataset by removing unneeded columns.The columns in the dataset are output in the same order as in the original data, even if you specify them in a different order.* Dataset output visualization featureIn Azure Machine Learning Designer, the "dataset output feature" refers to how a component's output data is registered as a dataset, which can be either a "File Dataset" or a "Tabular Dataset". File datasets include the output folder with data and meta files, useful for staying within the designer. Tabular datasets, on the other hand, register only the output data file in a format easily consumed by other tools like Automated Machine Learning or the Python SDK, making them suitable for use outside the designer.Note: Data preview and profileAfter you create your dataset, verify that you can view the preview and profile in the studio:1. Sign in to the Azure Machine Learning studio2. Under Assets in the left navigation, select Data as shown in the following screenshot:1. Select the name of the dataset you want to view.2. Select the Explore tab.3. Select the Preview tab, as shown in the following screenshot:1. Select the Profile tab, as shown in the following screenshot:To verify whether your data set is ML-ready, you can use summary statistics across your data set. For non- numeric columns, these statistics include only basic statistical measures - for example, min, max, and error count. Numeric columns offer statistical moments and estimated quantiles.The Azure Machine Learning dataset data profile includes:Note: Blank entries appear for features with irrelevant types.* FeatureThe summarized column name* ProfileIn-line visualization based on the inferred type. Strings, booleans, and dates have value counts. Decimals (numerics) have approximated histograms. These visualizations offer a quick understanding of the data distribution.* Etc.
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-connect-data-ui
What is an example of a regression model in machine learning?
In machine learning, a regression model is a predictive tool used to understand and model the relationship between a dependent variable (the outcome) and one or more independent variables (features). The goal is to predict a continuous numerical value based on the input features. Regression models are a type of supervised learning technique that uses statistical methods to learn patterns from data and make predictions.In Azure Machine Learning, a regression model is a supervised machine learning technique used to predict continuous numerical values. Unlike classification, which predicts categorical labels, regression models aim to establish relationships between independent variables and a continuous dependent variable, such as predicting house prices based on factors like square footage and location.
https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/linear-regression
DRAG DROP (Drag and Drop is not supported)Match the tasks to the appropriate machine learning models.To answer, drag the appropriate model from the column on the left to its scenario on the right. Each model may be used once, more than once, or not at all.Note: Each correct match is worth one point.Select and Place:
Box 1: ClassificationBox 2: RegressionPredict the amount of consumed fuel based on flight distance.In Azure Machine Learning, a regression model is a supervised machine learning technique used to predict continuous numerical values. Unlike classification, which predicts categorical labels, regression models aim to establish relationships between independent variables and a continuous dependent variable, such as predicting house prices based on factors like square footage and location.Box 3: ClusteringPredict whether a passenger will miss their flight based on demographic data.In Azure Machine Learning, clustering is an unsupervised machine learning technique that groups similar data points together into clusters. This is done by identifying patterns and relationships within the data without relying on labeled examples. Clustering is helpful for tasks like identifying customer segments, anomaly detection, and exploring data structures.Applications:* Customer Segmentation: Grouping customers into different segments based on their purchasing behavior or demographics.*-> Anomaly Detection: Identifying data points that deviate significantly from the norm.* Data Exploration: Uncovering hidden patterns and structures within the data.
What is a form of unsupervised machine learning?
Unsupervised learning is a type of machine learning where algorithms learn from unlabeled data, discovering patterns, structures, and relationships without explicit human supervision or guidance. Unlike supervised learning, where algorithms are trained on labeled data to predict outcomes, unsupervised learning aims to uncover hidden insights from data without predefined outcomes or labels.Applications:This type of learning is well-suited for tasks like:*-> Clustering: Grouping similar data points together based on their characteristics.Dimensionality Reduction: Identifying and reducing the number of important features or variables in a dataset.* Association Rule Mining: Discovering relationships between different elements in a dataset, often used in market basket analysis.* Anomaly Detection: Identifying data points that are significantly different from the norm, which can be helpful in fraud detection or identifying errors.
https://www.ibm.com/think/topics/unsupervised-learning
You have a dataset that contains the columns shown in the following table.You have a machine learning model that predicts the value of ColumnE based on the other numeric columns.Which type of model is this?
Azure regression models are designed and used for making predictions, particularly when the target variable is continuous. Azure's machine learning platform offers various regression algorithms and tools to train.
https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/linear-regression? view=azureml-api-2
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