Question 1 of 25
Hints left: 3
Q1
Which technique is used to improve model generalization?
Q2
What is the primary function of 'activation functions' in neural networks?
Q3
What is 'K-Fold Cross-Validation' used for?
Q4
What does 'feature scaling' ensure in machine learning?
Q5
Which algorithm is used for supervised learning problems?
Q6
What is 'mean absolute error' used to measure?
Q7
What is 'model ensembling'?
Q8
Which technique is used to handle multicollinearity?
Q9
Which of the following is a dimensionality reduction technique?
Q10
What is 'cross-entropy loss' commonly used for?
Q11
What is the purpose of 'dropout' in neural networks?
Q12
What does 'Bayesian Optimization' focus on?
Q13
What is 'Principal Component Analysis' (PCA) used for?
Q14
What is 'Dimensionality Reduction'?
Q15
What does 'Stochastic Gradient Descent' (SGD) use for optimization?
Q16
What does 'Feature Scaling' achieve in machine learning?
Q17
What is 'feature importance' in the context of decision trees?
Q18
What is 'Grid Search' used for in machine learning?
Q19
What does 'Logistic Regression' model predict?
Q20
Which technique is used to handle outliers in data?
Q21
What is the purpose of 'data augmentation'?
Q22
Which algorithm is used for dimensionality reduction in supervised learning?
Q23
What is the 'bias-variance tradeoff'?
Q24
What is the primary goal of 'regularization' techniques?
Q25
What is 'Mean Squared Error' (MSE) used to evaluate?