A. They improve model interpretability
B. They reduce model complexity
C. They maximize prediction accuracy
D. They require more training data
Answer: Option B
This Question Belongs to Data Science >> Machine Learning Algorithms
A. Linear Regression
B. K-Means Clustering
C. Decision Tree
D. Logistic Regression
In machine learning, what does the term "overfitting" refer to?
A. The model performs well on the test data
B. The model fits the training data perfectly
C. The model fails to generalize
D. The model is too simple to learn patterns
A. Support Vector Machine (SVM)
B. K-Means Clustering
C. Random Forest
D. Linear Regression
What is the primary goal of a decision tree algorithm in machine learning?
A. To minimize errors on the training data
B. To create a linear regression model
C. To visualize data relationships
D. To classify data points into categories
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