NOVEL THINKING

These short objective type questions with answers are very important for Board exams as well as competitive exams. A subdivision of a set of examples into a number of classes. For context, Yahoo Answers has 10 classes and supervised models get an accuracy in the mid 70s. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. Generally, we can divide all function approximation tasks into classification tasks and regression tasks. These tuples can also be referred to as sample, object or data points. The task of supervised classification involves extracting information from the labeled examples that allows accurate prediction of class labels of future examples. Using Classifier for Classification. In multi class classification each sample is assigned to one and only one target label. Supervisory positions in this series should be evaluated by reference to the General Schedule Supervisory Guide. This means that class labels or class membership are not mutually exclusive. Employee classification is established as part of the Fair Labor Standards Act (FLSA). A. Classification Predictive Modeling. All configurations follow the generic design present in architecture and differ only in the depth: from 11 weight layers in the network A (8 conv. Some classification tasks require predicting more than one class label. -Describe the core differences in analyses enabled by regression, classification, and clustering. B. Here the test data is used to estimate the accuracy of classification … Classification predictive modeling is the task of approximating a mapping function (f) from input variables (X) to discrete output variables (y). C. The task of assigning a classification to a set of examples. Classification task referred to. and 3 FC layers) to 19 weight layers in the network E (16 conv. -Represent your data as features to serve as input to machine learning models. Multi-class classification: Classification with more than two classes. The latter situation is often referred to as class imbalance, and obtaining high classification accuracy in the imbalanced case is usually more challenging than the balanced case. THERAPY AID, GS-0636-01 Nature of the assignment Therapy Aids GS-1 perform simple and repetitive tasks which require no previous experience. The company will observe any legal guidelines that apply to the classification of employees. D. None of these : Answer Report Discuss : Option: C. The FLSA policy determines which employees (exempt vs. non-exempt) are subject to minimum wage and overtime laws. Classification. Classification is a common machine learning task. Classification in Data Mining Objective Type Questions and Answers for competitive exams. The nets are referred to their names (A-E). Classification is a type of supervised learning in which models learn using training data, and apply those learnings to new data. These tasks are referred to as multiple label classification, or multi-label classification for short. The output variables are often called labels or categories. These short solved questions or quizzes are provided by Gkseries. A measure of the accuracy, of the classification of a concept that is given by a certain theory. the criteria in this standard with the application of sound position-classification judgment. In this step, the classifier is used for classification. Categorization tasks in which no labels are supplied are referred to as unsupervised classification, unsupervised learning, or data clustering. and 3 FC layers). Azure Machine Learning offers featurizations specifically for these tasks, such as deep neural network text featurizers for classification. On the Yahoo Answers topic classification task, we find an F1 of $46.9$ and $31.2$ with and without this projection step, respectively.

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