In data science, what is the primary goal of dimensionality reduction techniques like Principal Component Analysis (PCA)?
A. To reduce the number of features while preserving important information
B. To add more features to the dataset
C. To normalize data
D. To visualize data
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. Data Modeling
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