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Spark provides a machine learning library known as MLlib. Spark MLlib provides various machine learning algorithms such as classification, regression, clustering, and collaborative filtering. It also provides tools such as featurization, pipelines, persistence, and utilities for handling linear algebra operations, statistics and data handling. This course will start you off on your journey and walk you through some of the machine learning libraries and how to use them.
Module 1 – Spark MLlib Data Types
Question: Sparse Data generally contains many non-zero values, and few zero values.
Question: Local matrices are generally stored in distributed systems and rarely on single machines.
Question: Which of the following are distributed matrices?
Module 2 – Review of Algorithms
Question: Logistic Regression is an algorithm used for predicting numerical values.
Question: The SVM algorithm maximizes the margins between the generated hyperplane and two clusters of data.
Question: Which of the following is true about Gaussian Mixture Clustering?
Module 3 – Spark MLlib Decision Trees and Random Forests
Question: Which of the following is a stopping parameter in a Decision Tree?
Question: When using a regression type of Decision Tree or Random Forest, the value for impurity can be measured as either ‘entropy’ or ‘variance’.
Question: In a Random Forest, featureSubsetStrategy is considered a stopping parameter, but not a tunable parameter.
Module 4 – Spark MLlib Clustering
Question: In Spark MLlib, the initialization mode for the K-Means training method is called
Question: In K-Means, the “runs” parameter determines the number of data points allowed in each cluster.
Question: In Gaussian Mixture Clustering, the sum of all values outputted from the “weights” function must equal 1.
Question: In Gaussian Mixture Clustering, the predictSoft function provides membership values from the top three Gaussians only.
Question: In Decision Trees, what is true about the size of a dataset?
Question: A Logistic Regression algorithm is ineffective as a binary response predictor.
Question: What is the Row Pointer for a Matrix with the following Row Indices: [5, 1 | 6 | 2, 8, 10]
Question: For multiclass classification, try to use (M-1) Decision Tree split candidates whenever possible.
Question: In a Decision Tree, choosing a very large maxDepth value can:
Question: In Gaussian Mixture Clustering, a large value returned from the weights function represents a large precedence of that Gaussian.
Question: Increasing the value of epsilon when creating the K-Means Clustering model can:
Question: In order to train a machine learning model in Spark MLlib, the dataset must be in the form of a(n)
Question: What is true about Dense and Sparse Vectors?
Question: In a Decision Tree, increaing the maxBins parameter allows for more splitting candidates.
Question: In classification models, the value for the numClasses parameter does not depend on the data, and can change to increase model accuracy.
Question: What is true about Labeled Points?
Question: In the Gaussian Mixture Clustering model, the convergenceTol value is a stopping parameter that can be tuned, similar to epsilon in k-means clustering.
Question: In Gaussian Mixture Clustering, the “Gaussians” function outputs the coordinates of the largest Gaussian, as well as the standard deviation for each Gaussian in the mixture.
Question: What is true about the maxDepth parameter for Random Forests?
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