In reinforcement learning, the agent learns to take actions that maximize the cumulative __________.
- accuracy
- errors
- loss
- rewards
In reinforcement learning, the agent tries to maximize cumulative rewards through its actions.
Machine Learning is commonly used in ____________ to create personalized recommendations.
- Drug Development
- Recommender Systems
- Traffic Management
- Weather Prediction
Machine Learning is extensively used in Recommender Systems to create personalized recommendations, analyzing user behavior and preferences.
What are the underlying assumptions of LDA, and how do they affect the performance?
- Assumes different covariance matrices, normal distribution; affects adaptability
- Assumes equal class sizes; affects bias
- Assumes equal variance, non-normal distribution; affects robustness
- Assumes normal distribution, equal covariance matrices; affects classification accuracy
LDA assumes that the features are normally distributed and that the classes have equal covariance matrices. These assumptions, if met, lead to better "classification accuracy," but if violated, may lead to suboptimal performance.
__________ is a boosting algorithm that builds trees one at a time, where each tree corrects the errors of the previous one.
- AdaBoost
- Bagging
- Gradient Boosting
- Random Forest
AdaBoost is a boosting algorithm that builds trees sequentially, with each tree correcting the errors of the previous one by giving more weight to the misclassified instances.
What is the relationship between dimensionality reduction and the curse of dimensionality?
- Depends on the method
- Has no relationship
- Helps in mitigating the curse
- Intensifies the curse
The relationship between dimensionality reduction and the curse of dimensionality is that dimensionality reduction helps in mitigating the curse. The curse of dimensionality refers to the challenges that arise as the number of dimensions increases, and reducing dimensions can alleviate these problems.
You need to improve the performance of a weak learner. Which boosting algorithm would you select, and why?
- AdaBoost
- Any boosting algorithm will suffice
- Gradient Boosting without considering the loss function
- Random Boosting
AdaBoost is a boosting algorithm designed to improve the performance of weak learners. By adjusting the weights of misclassified instances and focusing on them in subsequent models, AdaBoost iteratively corrects errors and enhances the overall model's performance.
How is the Logit function related to Logistic Regression?
- It is a type of cost function
- It is an alternative name for Logistic Regression
- It's the inverse of the Sigmoid function and maps probabilities to log-odds
- It's used for multi-class classification
In Logistic Regression, the Logit function is the inverse of the Sigmoid function. It maps probabilities to log-odds and forms the link function in logistic modeling.
In Decision Trees, the __________ is used to measure the impurity of a data partition or set.
- Accuracy
- Bias
- Gini Index
- Training set
In Decision Trees, the Gini Index is used to measure the impurity or disorder of a data partition or set. A lower Gini Index value indicates a purer node, and it is used to determine the best splits.
What are some advanced techniques to prevent overfitting in a deep learning model?
- Regularization, Dropout, Early Stopping, Data Augmentation
- Regularization, Dropout, Early Stopping, Over-sampling
- Regularization, Dropout, Late Stopping, Data Augmentation
- Regularization, Over-sampling, Early Stopping, Data Reduction
Advanced techniques such as "Regularization, Dropout, Early Stopping, and Data Augmentation" help in preventing overfitting by adding constraints, randomly deactivating neurons, halting training, and expanding the dataset, respectively.
Describe the process of Bootstrapping and its applications in model evaluation.
- Repeated sampling with replacement for bias reduction
- Repeated sampling with replacement for variance reduction
- Repeated sampling with replacement to estimate statistics and evaluate models
- Repeated sampling without replacement for model validation
Bootstrapping involves repeated sampling with replacement to estimate statistics and evaluate models. By creating numerous "bootstrap samples," it allows the calculation of standard errors, confidence intervals, and other statistical properties, even with a small dataset. It's valuable for model evaluation, hypothesis testing, and providing insight into the estimator's distribution.