You are working with a large dataset, and you want to reduce its dimensionality using PCA. How would you decide the number of principal components to retain, considering the amount of variance explained?
- By always retaining all principal components
- By always selecting the first two components
- By consulting with domain experts
- By retaining components explaining at least a predetermined threshold of variance
The number of principal components to retain can be decided based on a predetermined threshold of variance explained. For example, you may choose to keep components that together explain at least 95% of the total variance.
The ________ measures the average of the squares of the errors, while the ________ takes the square root of that average in regression analysis.
- MAE, MSE
- MSE, RMSE
- R-Squared, MAE
- RMSE, MAE
The Mean Squared Error (MSE) calculates the average of the squared differences between predicted and actual values, and the Root Mean Squared Error (RMSE) takes the square root of that average. RMSE gives more weight to large errors and is more interpretable as it is in the same unit as the response variable.
What is classification in the context of Machine Learning?
- Calculating numerical values
- Finding relationships between variables
- Grouping data into clusters
- Predicting discrete categories
Classification is the process of predicting discrete categories or labels for given input data in machine learning. It divides the data into predefined classes or groups.
How does the Root Mean Squared Error (RMSE) differ from Mean Squared Error (MSE)?
- RMSE is half of MSE
- RMSE is the square of MSE
- RMSE is the square root of MSE
- RMSE is the sum of MSE
The Root Mean Squared Error (RMSE) is the square root of the Mean Squared Error (MSE). While MSE measures the average squared differences, RMSE provides a value in the same unit as the original data. This makes RMSE more interpretable and commonly used when comparing model performance.
ElasticNet is a hybrid regularization technique that combines the L1 penalty of ________ and the L2 penalty of ________.
- ElasticNet, Ridge
- Lasso, Ridge
- Ridge, Lasso
- nan
ElasticNet combines the L1 penalty of Lasso and the L2 penalty of Ridge, providing a middle ground between the two techniques.
What is the primary purpose of using regularization techniques in Machine Learning models?
- Enhance data visualization
- Increase accuracy
- Increase model complexity
- Reduce overfitting
Regularization techniques are used to prevent overfitting by adding constraints to the model, thus helping it to generalize better on unseen data.
____________ Learning, a subset of Machine Learning, is essential in training robots to perform specific tasks in manufacturing industries.
- Disease Prediction
- Fraud Detection
- Reinforcement
- Weather Prediction
Reinforcement Learning, a subset of Machine Learning, plays a crucial role in training robots for specific tasks in manufacturing through trial and error and receiving feedback from the environment.
Explain how cross-validation can be used to mitigate the risk of overfitting.
- By adding noise to the training data
- By increasing model complexity
- By reducing model complexity
- By splitting the data into multiple subsets and training on different combinations
Cross-validation mitigates the risk of overfitting "by splitting the data into multiple subsets and training on different combinations." It ensures that the model is evaluated on unseen data and helps in tuning hyperparameters without relying on the test set.
The ___________ matrix in LDA represents the spread of the individual classes around their mean.
- between-class scatter
- correlation
- covariance
- within-class scatter
The "within-class scatter" matrix in LDA represents the spread of the individual classes around their mean, capturing the within-class variance.
What role does the regularization parameter 'C' play in an SVM?
- Controls balance between margin maximization and error minimization
- Controls visualization
- Increases computation time
- Reduces data size
The regularization parameter 'C' in SVM controls the trade-off between maximizing the margin and minimizing classification errors.
What is the difference between Gradient Boosting and AdaBoost?
- Both are the same
- Both focus on increasing bias
- Gradient Boosting is for regression, AdaBoost is for classification
- Gradient Boosting uses gradients to correct errors, while AdaBoost focuses on weighting misclassified instances
Gradient Boosting builds models sequentially, using gradients to correct errors and minimize the loss function. AdaBoost, on the other hand, adjusts the weights of misclassified instances to focus the next model on them. Both aim to improve performance but use different approaches.
Explain how the learning agent interacts with the environment in Reinforcement Learning.
- Through clustering
- Through combining labeled and unlabeled data
- Through labeled data
- Through rewards and penalties
The agent in Reinforcement Learning interacts with the environment through rewards and penalties, learning to take actions to achieve a goal.