C1000-144 Dumps
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IBM Machine Learning Data Scientist v1
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Which of the following metrics is commonly used to monitor model performance in production?
Mean Absolute Error (MAE)
Precision-Recall curve
rea Under the ROC Curve (AUC-ROC) wer: D
anation: The Area Under the ROC Curve (AUC-ROC) is a commonly ic for monitoring model performance in production. It provides a mea model's ability to discriminate between positive and negative instan
ng it suitable for binary classification problems.
When refining a machine learning model, which of the following techniqu e used for regularization?
regularization (Lasso) radient boosting
ropout regularization nsemble learning
R-squared (R2) score
A
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Answer: A
Explanation: L1 regularization, also known as Lasso regularization, adds a penalty term to the model's loss function to encourage sparsity in the feature weights. It helps in selecting the most relevant features and prevents overfitting.
When evaluating a business problem for machine learning implementation, which of the following ethical implications should be considered?
Privacy concerns and data protection
ocial biases and fairness in decision-making nvironmental sustainability and resource consumption
wer: A
anation: When evaluating a business problem for machine learning ementation, it is crucial to consider ethical implications. Privacy conc ata protection should be addressed to ensure that personal and sensiti mation is handled securely and in compliance with relevant regulatio
When monitoring models in production, which of the following techniques sed for detecting data drift?
rincipal Component Analysis (PCA) means clustering
atistical hypothesis testing
Market competition and intellectual property rights
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Ensemble learning Answer: C
can
Explanation: Statistical hypothesis testing can be used to detect data drift by comparing the statistical properties of the new data with the reference data. It helps in identifying changes in the data distribution and triggers appropriate
actions for model adaptation or retraining.
What is an important consideration when monitoring machine learning models in production?
ontinuously evaluating model fairness and bias ebuilding the model periodically with new data educing the number of model performance metrics
wer: B
anation: When monitoring machine learning models in production, it i ntial to continuously evaluate and mitigate any biases or unfairness in el's predictions. This helps in ensuring ethical and unbiased decision- ng.
ch of the following methods can be used for model explainability? artial dependence plots
ackpropagation algorithm upport Vector Machines (SVM)
Tracking model accuracy on the training data
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Random Forest feature importance Answer: A
Explanation: Partial dependence plots are a technique used for model explainability. They show how the model's predictions change as a particular feature varies while holding other features constant. By visualizing the
relationship between individual features and the predicted outcome, partial dependence plotsprovide insights into the model's behavior and help in understanding its decision-making process.
ecursive Feature Elimination (RFE) rid search for hyperparameter tuning means clustering for feature grouping ross-validation for model evaluation
wer: A
anation: Recursive Feature Elimination (RFE) is a technique used for re selection, which recursively removes features and builds models us emaining features. It ranks the features based on their importance and ts the optimal subset of features for the model.
ch of the following activities is part of the model deployment process? raining the model on the entire dataset
To implement the proper model, which of the following techniques can be used for feature selection?
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C
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Evaluating the model's performance on a validation set
Applying the model to new, unseen data
Conducting exploratory data analysis Answer: C
Explanation: Model deployment involves applying the trained model to new,
unseen data for making predictions or generating insights. This step is crucial to assess the model's performance in real-world scenarios.
During exploratory data analysis, which of the following techniques can be used for data preparation?
eature scaling and normalization rincipal Component Analysis (PCA)
eature extraction and dimensionality reduction utlier detection and removal
wer: C
anation: During exploratory data analysis, feature extraction and nsionality reduction techniques can be employed to identify meaningf res and reduce the dimensionality of the dataset. This helps in improv odel's performance and reducing computational complexity.
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