The addition of _________ in the loss function is a common technique to regularize the model and prevent overfitting.
- bias
- learning rate
- regularization terms
- weights
Regularization terms (like L1 or L2 penalties) in the loss function constrain the model, reducing the risk of overfitting by preventing large weights.
You're given a dataset with several features, some of which are highly correlated. How would you handle this using dimensionality reduction techniques?
- Applying K-Means Clustering
- Applying L1 Regularization
- Applying Principal Component Analysis (PCA)
- Applying Random Forest
Principal Component Analysis (PCA) would be used to handle high correlation among features. It reduces dimensionality by creating new uncorrelated variables that capture the variance present in the original features.
Explain the Bias-Variance tradeoff in the context of Cross-Validation.
- Increasing k decreases bias but may increase variance
- Increasing k decreases both bias and variance
- Increasing k increases bias but decreases variance
- Increasing k increases both bias and variance
The Bias-Variance tradeoff in the context of k-fold Cross-Validation refers to the balance between bias (error due to overly simplistic assumptions) and variance (error due to excessive complexity). Increasing k generally decreases bias since more data is used for training, but it may lead to an increase in variance as the validation set becomes more similar to the training set.
What is the primary difference between the Gini Index and entropy when used in Decision Trees?
- Calculation Method
- Complexity
- Scale
- Units
Gini Index and entropy are both used to measure purity, but they are calculated differently. Entropy uses logarithms, while Gini Index does not.
Clustering is a common task in __________ learning, where data is grouped based on inherent similarities without the use of labels.
- reinforcement
- semi-supervised
- supervised
- unsupervised
Unsupervised learning commonly involves clustering, where data is grouped based on similarities without using labels.
Can you explain how the choice of kernel in SVM affects the decision boundary?
- Changes the color
- Increases computation time
- Reduces data size
- Transforms the feature space
Different kernels transform the feature space in different ways, affecting the shape and position of the decision boundary in SVM.
You need to build a recommendation system with a clear explanation of why certain recommendations are made. How would you approach this considering AI, Machine Learning, and Deep Learning?
- AI, for its rule-based logic
- Deep Learning, for its data-driven insights
- Machine Learning, for its predictive analysis
- nan
Traditional AI models, often based on rule-based logic, may provide clear explanations for recommendations, whereas Machine Learning and Deep Learning models may be less interpretable.
How do Ridge and Lasso regularization techniques interact with Polynomial Regression to mitigate overfitting?
- By adding a penalty term to constrain coefficients
- By fitting low-degree polynomials
- By ignoring interaction terms
- By increasing the model's complexity
Ridge and Lasso regularization techniques mitigate overfitting in Polynomial Regression by adding a penalty term to the loss function. This constrains the coefficients, reducing the complexity of the model, and helps in avoiding overfitting.
How can Cross-Validation help in hyperparameter tuning?
- By allowing repeated testing on the same validation set
- By improving model accuracy directly
- By providing robust performance estimates to select the best hyperparameters
- By reducing computation time
Cross-Validation enables hyperparameter tuning by providing a robust estimate of the model's performance across different data splits. This process helps to find hyperparameters that generalize well to unseen data, minimizing the risk of overfitting, and allowing a more informed selection of optimal hyperparameters.
What is the main goal of Artificial Intelligence as a scientific discipline?
- To create complex algorithms
- To improve data storage
- To make money through technology
- To mimic human intelligence and decision-making
The primary goal of Artificial Intelligence is to develop systems that can mimic human intelligence and decision-making processes.
What's the difference between simple linear regression and multiple linear regression?
- One is for classification, the other for regression
- One is linear, the other is nonlinear
- One uses one predictor, the other uses more than one
- One uses time series data, the other doesn't
Simple linear regression involves one independent variable to predict the dependent variable, while multiple linear regression uses two or more independent variables for prediction.
When a Decision Tree is too complex and fits the training data too well, __________ techniques can be applied to simplify the model.
- Bagging
- Boosting
- Normalizing
- Pruning
When a Decision Tree is overfitting (too complex), pruning techniques can be applied to simplify the model. Pruning involves removing branches that have little predictive power, thereby reducing the complexity and the risk of overfitting.