You are performing clustering by using the K-means algorithm.You need to define the possible termination conditions.Which three conditions can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.
Answer(s): A,C,D
AD: The algorithm terminates when the centroids stabilize or when a specified number of iterations are completed.C: A measure of how well the centroids represent the members of their clusters is the residual sum of squares or RSS, the squared distance of each vector from its centroid summed over all vectors. RSS is the objective function and our goal is to minimize it.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/k-means-clustering https://nlp.stanford.edu/IR-book/html/htmledition/k-means-1.html
HOTSPOT (Drag and Drop is not supported)You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification usingPython code shown below:You need to evaluate the C-Support Vector classification code.Which evaluation statement should you use? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: Automatically adjust weights inversely proportional to class frequencies in the input dataThe ג€balancedג€ mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).Box 2: Penalty parameter Parameter: C : float, optional (default=1.0)Penalty parameter C of the error term.
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
You are building a machine learning model for translating English language textual content into French language textual content.You need to build and train the machine learning model to learn the sequence of the textual content.Which type of neural network should you use?
Answer(s): C
To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both. They're called recurrent because the network's hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory.It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.
https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571
You create a binary classification model.You need to evaluate the model performance.Which two metrics can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.
Answer(s): B,C
The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.Note: A very natural question is: 'Out of the individuals whom the model, how many were classified correctly (TP)?'This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
You create a script that trains a convolutional neural network model over multiple epochs and logs the validation loss after each epoch. The script includes arguments for batch size and learning rate.You identify a set of batch size and learning rate values that you want to try.You need to use Azure Machine Learning to find the combination of batch size and learning rate that results in the model with the lowest validation loss.What should you do?
Answer(s): E
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
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