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