In the context of machine learning, what is the purpose of regularization techniques such as L1 and L2 regularization?
A. To prevent overfitting by adding a penalty term to the loss function
B. To remove outliers from the data
C. To increase model complexity
D. To reduce dimensionality
Answer: Option A
This Question Belongs to Data Science >> Data Science Miscellaneous
A. Chi-squared test
B. T-test
C. ANOVA
D. Regression analysis
In the context of data ethics, what does "bias mitigation" refer to?
A. Increasing the sample size
B. Improving model accuracy
C. Removing outliers from a dataset
D. Reducing biases in data collection
What does the term "overfitting" mean in machine learning?
A. The model fits the training data too closely and performs poorly on new data
B. The model generalizes well to new data
C. The model is too simple and underperforms on the training data
D. The model is perfectly accurate on all data
In the CRISP-DM data mining process model, what does "DM" stand for?
A. Data Modeling
B. Data Mining
C. Data Manipulation
D. None of the above
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